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1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"ebooks_amp_whitepapers","context":"resources","tag":"link","label":"ebooks_amp_whitepapers_link_resources_navbar"}" href="https://github.com/resources/whitepapers"> Ebooks & Whitepapers </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"customer_stories","context":"resources","tag":"link","label":"customer_stories_link_resources_navbar"}" href="https://github.com/customer-stories"> Customer Stories </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"partners","context":"resources","tag":"link","label":"partners_link_resources_navbar"}" href="https://partner.github.com"> Partners <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg><span data-view-component="true" class="d-inline"> Star </span> <span id="repo-stars-counter-star" aria-label="433 users starred this repository" data-singular-suffix="user starred this repository" data-plural-suffix="users starred this repository" data-turbo-replace="true" title="433" data-view-component="true" class="Counter js-social-count">433</span> </a></div> </li> </ul> </div> </div> <div id="responsive-meta-container" data-turbo-replace> <div class="d-block d-md-none mb-2 px-3 px-md-4 px-lg-5"> <p class="f4 mb-3 "> High-quality pro HDR image resizing / scaling C++ library, including a very fast, precise, SIMD Lanczos resizer (header-only C++) </p> <h3 class="sr-only">License</h3> <div 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":42787820,"defaultBranch":"master","name":"avir","ownerLogin":"avaneev","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2015-09-19T20:04:39.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/11453377?v=4","public":true,"private":false,"isOrgOwned":false},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1633242686.452466","canEdit":false,"refType":"branch","currentOid":"d4884eeb8e9969e84eb398443aeb92202659e584"},"tree":{"items":[{"name":"bin","path":"bin","contentType":"directory"},{"name":"other","path":"other","contentType":"directory"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"avir.h","path":"avir.h","contentType":"file"},{"name":"avir_dil.h","path":"avir_dil.h","contentType":"file"},{"name":"avir_float4_sse.h","path":"avir_float4_sse.h","contentType":"file"},{"name":"avir_float8_avx.h","path":"avir_float8_avx.h","contentType":"file"},{"name":"lancir.h","path":"lancir.h","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":9,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/avaneev/avir/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/avaneev/avir.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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C++)\u003c/h1\u003e\u003ca id=\"user-content-avir---image-resizing-algorithm-in-c\" class=\"anchor\" aria-label=\"Permalink: AVIR - Image Resizing Algorithm (in C++)\" href=\"#avir---image-resizing-algorithm-in-c\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMe, Aleksey Vaneev, is happy to offer you an open source image resizing /\nscaling library which has reached a production level of quality, and is\nready to be incorporated into any project. This library features routines\nfor both down- and upsizing of 8- and 16-bit, 1 to 4-channel images. Image\nresizing routines were implemented in a portable, cross-platform, header-only\nC++ code, and have a high level of optimality. Beside resizing, this library\noffers a sub-pixel shift operation. Built-in sRGB gamma correction is\navailable.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe resizing algorithm at first produces 2X upsized image (relative to the\nsource image size, or relative to the destination image size if downsizing is\nperformed) and then performs interpolation using a bank of sinc function-based\nfractional delay filters. At the last stage a correction filter is applied\nwhich fixes smoothing introduced at previous steps.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe resizing algorithm was designed to provide the best visual quality. The\nauthor even believes this algorithm provides the \"ultimate\" level of\nquality (for an orthogonal, non neural-network, resizing) which cannot be\nincreased further: no math exists to provide a better frequency response,\nbetter anti-aliasing quality and at the same time having less ringing\nartifacts: these are 3 elements that define any resizing algorithm's quality;\nin AVIR practice these elements have a high correlation to each other, so they\ncan be represented by a single parameter (AVIR offers several parameter sets\nwith varying quality). Algorithm's time performance turned out to be very good\nas well (for the \"ultimate\" image quality).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAn important element utilized by this algorithm is the so called Peaked Cosine\nwindow function, which is applied over sinc function in all filters. Please\nconsult the documentation for more details.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote that since AVIR implements orthogonal resizing, it may exhibit diagonal\naliasing artifacts. These artifacts are usually suppressed by EWA or radial\nfiltering techniques. EWA-like technique is not implemented in AVIR, because\nit requires considerably more computing resources and may produce a blurred\nimage.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs a bonus, a much faster \u003ccode\u003eLANCIR\u003c/code\u003e image resizing algorithm is also offered as\na part of this library. But the main focus of this documentation is the\noriginal AVIR image resizing algorithm.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eAVIR is dedicated to women. Your digital photos can look good at any size!\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eP.S. Please credit the author of this library in your documentation in the\nfollowing way: \"AVIR image resizing algorithm designed by Aleksey Vaneev\".\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAffine and Non-Linear Transformations\u003c/h2\u003e\u003ca id=\"user-content-affine-and-non-linear-transformations\" class=\"anchor\" aria-label=\"Permalink: Affine and Non-Linear Transformations\" href=\"#affine-and-non-linear-transformations\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAVIR does not offer affine and non-linear image transformations \"out of the\nbox\". Since upsizing is a relatively fast operation in AVIR (required time\nscales linearly with the output image area), affine and non-linear\ntransformations can be implemented in steps: 4- to 8-times upsizing,\ntransformation via bilinear interpolation, downsizing (linear proportional\naffine transformations can probably skip the downsizing step). This should not\ncompromise the transformation quality much as bilinear interpolation's\nproblems will mostly reside in spectral area without useful signal, with a\nmaximum of 0.7 dB high-frequency attenuation for 4-times upsizing, and 0.17 dB\nattenuation for 8-times upsizing. This approach is probably as time efficient\nas performing a high-quality transform over the input image directly (the only\nserious drawback is the increased memory requirement). Note that affine\ntransformations that change image proportions should first apply proportion\nchange during upsizing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eC++ compiler and system with efficient \"float\" floating-point (24-bit\nmantissa) type support. This library can also internally use the \"double\" and\nSIMD floating-point types during resizing if needed. This library does not\nhave dependencies beside the standard C library.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage Information\u003c/h2\u003e\u003ca id=\"user-content-usage-information\" class=\"anchor\" aria-label=\"Permalink: Usage Information\" href=\"#usage-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe image resizer is represented by the \u003ccode\u003eavir::CImageResizer\u0026lt;\u0026gt;\u003c/code\u003e class, which\nis a single front-end class for the whole library. Basically, you do not need\nto use nor understand any other classes beside this class.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.voxengo.com/public/avir/Documentation/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe code of the library resides in the \"avir\" C++ namespace, effectively\nisolating it from all other code. The code is thread-safe. You need just\na single resizer object per running application, at any time, even when\nresizing images concurrently.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo resize images in your application, simply add 3 lines of code (note that\nyou may need to change \u003ccode\u003eImageResizer( 8 )\u003c/code\u003e here, to specify your image's true\nbit resolution, which may be 10 or even 16):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#include \u0026quot;avir.h\u0026quot;\navir :: CImageResizer\u0026lt;\u0026gt; ImageResizer( 8 );\nImageResizer.resizeImage( InBuf, 640, 480, 0, OutBuf, 1024, 768, 3, 0 );\n(multi-threaded operation requires additional coding, see the documentation)\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eavir.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\navir :: CImageResizer\u0026lt;\u0026gt; \u003cspan class=\"pl-en\"\u003eImageResizer\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e );\nImageResizer.resizeImage( InBuf, \u003cspan class=\"pl-c1\"\u003e640\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e480\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, OutBuf, \u003cspan class=\"pl-c1\"\u003e1024\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e768\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e );\n(multi-threaded operation requires additional coding, see the documentation)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAVIR works with header-less \"raw\" image buffers. If you are not too familiar\nwith the low-level \"packed interleaved\" image storage format, the \u003ccode\u003eInBuf\u003c/code\u003e is\nexpected to be \u003ccode\u003ew*h*c\u003c/code\u003e elements in size, where \u003ccode\u003ew\u003c/code\u003e and \u003ccode\u003eh\u003c/code\u003e is the width and\nthe height of the image in pixels, respectively, and \u003ccode\u003ec\u003c/code\u003e is the number of\ncolor channels in the image. In the example above, the size of the \u003ccode\u003eInBuf\u003c/code\u003e is\n\u003ccode\u003e640*480*3=921600\u003c/code\u003e elements. If you are working with 8-bit images, the buffer\nand the elements should have the \u003ccode\u003euint8_t*\u003c/code\u003e type; if you are working with\n16-bit images, they should have the \u003ccode\u003euint16_t*\u003c/code\u003e type. Note that when\nprocessing 16-bit images, the value of \u003ccode\u003e16\u003c/code\u003e should be used in resizer's\nconstructor. AVIR's algorithm does not discern between channel packing order\n(\u003ccode\u003eRGBA\u003c/code\u003e, \u003ccode\u003eARGB\u003c/code\u003e, \u003ccode\u003eBGRA\u003c/code\u003e, etc.), so if the \u003ccode\u003eBGRA\u003c/code\u003e ordered elements were passed\nto it, the result will be also \u003ccode\u003eBGRA\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf the graphics library you are using returns a \u003ccode\u003euint32_t*\u003c/code\u003e pointer to a raw\n4-channel packed pixel data, you will need to cast both the input and output\npointers to the \u003ccode\u003euint8_t*\u003c/code\u003e type when supplying them to the resizing function,\nand set the ElCount to 4.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor low-ringing performance:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"avir :: CImageResizer\u0026lt;\u0026gt; ImageResizer( 8, 0, avir :: CImageResizerParamsLR() );\"\u003e\u003cpre\u003eavir :: CImageResizer\u0026lt;\u0026gt; \u003cspan class=\"pl-en\"\u003eImageResizer\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, avir :: CImageResizerParamsLR() );\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo use the built-in gamma correction, which is disabled by default, an object\nof the \u003ccode\u003eavir::CImageResizerVars\u003c/code\u003e class with its variable \u003ccode\u003eUseSRGBGamma\u003c/code\u003e set to\n\u003ccode\u003etrue\u003c/code\u003e should be supplied to the \u003ccode\u003eresizeImage()\u003c/code\u003e function. Note that, when\nenabled, the gamma correction is applied to all channels (e.g. alpha-channel)\nin the current implementation.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"avir :: CImageResizerVars Vars;\nVars.UseSRGBGamma = true;\"\u003e\u003cpre\u003eavir :: CImageResizerVars Vars;\nVars.UseSRGBGamma = \u003cspan class=\"pl-c1\"\u003etrue\u003c/span\u003e;\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDithering (error-diffusion dither which is perceptually good) can be enabled\nthis way:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"typedef avir :: fpclass_def\u0026lt; float, float,\n avir :: CImageResizerDithererErrdINL\u0026lt; float \u0026gt; \u0026gt; fpclass_dith;\navir :: CImageResizer\u0026lt; fpclass_dith \u0026gt; ImageResizer( 8 );\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003etypedef\u003c/span\u003e avir :: fpclass_def\u0026lt; \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e, \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e,\n avir :: CImageResizerDithererErrdINL\u0026lt; \u003cspan class=\"pl-k\"\u003efloat\u003c/span\u003e \u0026gt; \u0026gt; fpclass_dith;\navir :: CImageResizer\u0026lt; fpclass_dith \u0026gt; \u003cspan class=\"pl-en\"\u003eImageResizer\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e );\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe library is able to process images of any bit depth: this includes 8-bit,\n16-bit, float and double types. Larger integer and signed integer types are\nnot supported. Supported source and destination image sizes are only limited\nby the available system memory. Note that the resizing function applies\nclipping to integer output only; floating-point output will not be clipped to\n[0; 1] range.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe code of this library was commented in the \u003ca href=\"http://www.doxygen.org/\" rel=\"nofollow\"\u003eDoxygen\u003c/a\u003e\nstyle. To generate the documentation locally you may run the\n\u003ccode\u003edoxygen ./other/avirdoxy.txt\u003c/code\u003e command from the library's directory. Note that\nthe code was suitably documented allowing you to make modifications, and to\ngain full understanding of the algorithm.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePreliminary tests show that this library (compiled with Intel C++ Compiler\n18.2 with AVX2 instructions enabled, without explicit SIMD resizing code) can\nresize 8-bit RGB 5184x3456 (17.9 Mpixel) 3-channel image down to 1920x1280\n(2.5 Mpixel) image in 245 milliseconds, utilizing a single thread, on Intel\nCore i7-7700K processor-based system without overclocking. This scales down to\n74 milliseconds if 8 threads are utilized.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMulti-threaded operation is not provided by this library \"out of the box\".\nThe multi-threaded (horizontally-threaded) infrastructure is available, but\nrequires additional system-specific interfacing code for engagement.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSIMD Usage Information\u003c/h2\u003e\u003ca id=\"user-content-simd-usage-information\" class=\"anchor\" aria-label=\"Permalink: SIMD Usage Information\" href=\"#simd-usage-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis library is capable of using SIMD floating-point types for internal\nvariables. This means that up to 4 color channels can be processed in\nparallel. Since the default interleaved processing algorithm itself remains\nnon-SIMD, the use of SIMD internal types is not practical for 1- and 2-channel\nimage resizing (due to overhead). SIMD internal type can be used this way:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#include \u0026quot;avir_float4_sse.h\u0026quot;\navir :: CImageResizer\u0026lt; avir :: fpclass_float4 \u0026gt; ImageResizer( 8 );\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eavir_float4_sse.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\navir :: CImageResizer\u0026lt; avir :: fpclass_float4 \u0026gt; \u003cspan class=\"pl-en\"\u003eImageResizer\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e );\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor 1-channel and 2-channel image resizing when AVX instructions are allowed\nit may be reasonable to utilize de-interleaved SIMD processing algorithm.\nWhile it gives no performance benefit if the \"float4\" SSE processing type is\nused, it offers some performance boost if the \"float8\" AVX processing type is\nused (given dithering is not performed, or otherwise performance is reduced at\nthe dithering stage since recursive dithering cannot be parallelized). The\ninternal type remains non-SIMD \"float\". De-interleaved algorithm can be used\nthis way:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-c++ notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"#include \u0026quot;avir_float8_avx.h\u0026quot;\navir :: CImageResizer\u0026lt; avir :: fpclass_float8_dil \u0026gt; ImageResizer( 8 );\"\u003e\u003cpre\u003e#\u003cspan class=\"pl-k\"\u003einclude\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eavir_float8_avx.h\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\navir :: CImageResizer\u0026lt; avir :: fpclass_float8_dil \u0026gt; \u003cspan class=\"pl-en\"\u003eImageResizer\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e );\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIt's important to note that on the latest Intel processors (i7-7700K and\nprobably later) the use of the aforementioned SIMD-specific resizing code may\nnot be justifiable, or may be even counter-productive due to many factors:\nmemory bandwidth bottleneck, increased efficiency of processor's circuitry\nutilization and out-of-order execution, automatic SIMD optimizations performed\nby the compiler. This is at least true when compiling 64-bit code with Intel\nC++ Compiler 18.2 with /QxSSE4.2, or especially with the /QxCORE-AVX2 option.\nSSE-specific resizing code may still be a little bit more efficient for\n4-channel image resizing.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotes\u003c/h2\u003e\u003ca id=\"user-content-notes\" class=\"anchor\" aria-label=\"Permalink: Notes\" href=\"#notes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis library was tested for compatibility with \u003ca href=\"http://gcc.gnu.org/\" rel=\"nofollow\"\u003eGNU C++\u003c/a\u003e,\n\u003ca href=\"http://www.microsoft.com/visualstudio/eng/products/visual-studio-express-products\" rel=\"nofollow\"\u003eMicrosoft Visual C++\u003c/a\u003e,\n\u003ca href=\"https://llvm.org/\" rel=\"nofollow\"\u003eLLVM\u003c/a\u003e, and \u003ca href=\"http://software.intel.com/en-us/c-compilers\" rel=\"nofollow\"\u003eIntel C++\u003c/a\u003e\ncompilers, on 32- and 64-bit Windows, macOS, and CentOS Linux. The code was\nalso tested with Dr.Memory/Win32 for the absence of uninitialized or\nunaddressable memory accesses.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAll code is fully \"inline\", without the need to compile any source files. The\nmemory footprint of the library itself is very modest, except that the size of\nthe temporary image buffers depends on the input and output image sizes, and\nis proportionally large.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe \"heart\" of resizing algorithm's quality resides in the parameters defined\nvia the \u003ccode\u003eavir::CImageResizerParams\u003c/code\u003e structure. While the default set of\nparameters that offers a good quality was already provided, there is\n(probably) still a place for improvement exists, and the default parameters\nmay change in a future update. If you need to recall an exact set of\nparameters, simply save them locally for a later use.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen the algorithm is run with no resizing applied (k=1), the result of\nresizing will not be an exact, but a very close copy of the source image. The\nreason for such inexactness is that the image is always low-pass filtered at\nfirst to reduce aliasing during subsequent resizing, and at last filtered by a\ncorrection filter. Such approach allows algorithm to maintain a stable level\nof quality regardless of the resizing \"k\" factor used.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis library includes a binary command line tool \"imageresize\" for some\ndesktop platforms. This tool was designed to be used as a demonstration of\nlibrary's performance, and as a reference, it is multi-threaded (the \u003ccode\u003e-t\u003c/code\u003e\nswitch can be used to control the number of threads utilized). This tool uses\nplain \"float\" processing (no explicit SIMD) and relies on automatic compiler\noptimizations. This tool uses the following libraries:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eturbojpeg Copyright (c) 2009-2013 D. R. Commander\u003c/li\u003e\n\u003cli\u003elibpng Copyright (c) 1998-2013 Glenn Randers-Pehrson\u003c/li\u003e\n\u003cli\u003ezlib Copyright (c) 1995-2013 Jean-loup Gailly and Mark Adler\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eNote that you can enable gamma-correction with the \u003ccode\u003e-g\u003c/code\u003e switch. However,\nsometimes gamma-correction produces \"greenish/reddish/bluish haze\" since\nlow-amplitude oscillations produced by resizing at object boundaries are\namplified by gamma correction. This can also have an effect of reduced\ncontrast.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInterpolation Discussion\u003c/h2\u003e\u003ca id=\"user-content-interpolation-discussion\" class=\"anchor\" aria-label=\"Permalink: Interpolation Discussion\" href=\"#interpolation-discussion\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe use of certain low-pass filters and 2X upsampling in this library is\nhardly debatable, because they are needed to attain a certain anti-aliasing\neffect and keep ringing artifacts low. But the use of sinc function-based\ninterpolation filter that is 18 taps-long (may be higher, up to 36 taps in\npractice) can be questioned, because such interpolation filter requires 18\nmultiply-add operations. Comparatively, an optimal Hermite or cubic\ninterpolation spline requires 8 multiply and 11 add operations.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOne of the reasons 18-tap filter is preferred, is because due to memory\nbandwidth limitations using a lower-order filter does not provide any\nsignificant performance increase (e.g. 14-tap filter is less than 5% more\nefficient overall). At the same time, in comparison to cubic spline, 18-tap\nfilter embeds a low-pass filter that rejects signal above 0.5*pi (provides\nadditional anti-aliasing filtering), and this filter has a consistent shape at\nall fractional offsets. Splines have a varying low-pass filter shape at\ndifferent fractional offsets (e.g. no low-pass filtering at 0.0 offset,\nand maximal low-pass filtering at 0.5 offset). 18-tap filter also offers a\nsuperior stop-band attenuation which almost guarantees absence of artifacts if\nthe image is considerably sharpened afterwards.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy 2X upsizing in AVIR?\u003c/h2\u003e\u003ca id=\"user-content-why-2x-upsizing-in-avir\" class=\"anchor\" aria-label=\"Permalink: Why 2X upsizing in AVIR?\" href=\"#why-2x-upsizing-in-avir\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eClassic approaches to image resizing do not perform an additional 2X upsizing.\nSo, why such upsizing is needed at all in AVIR? Indeed, image resizing can be\nimplemented using a single interpolation filter which is applied to the source\nimage directly. However, such approach has limitations:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst of all, speaking about non-2X-upsized resizing, during upsizing the\ninterpolation filter has to be tuned to a frequency close to pi (Nyquist) in\norder to reduce high-frequency smoothing: this reduces the space left for\nfilter optimization. Beside that, during downsizing, a filter that performs\nwell and predictable when tuned to frequencies close to the Nyquist frequency,\nmay become distorted in its spectral shape when it is tuned to lower\nfrequencies. That is why it is usually a good idea to have filter's stop-band\nbegin below Nyquist so that the transition band's shape remains stable at any\nlower-frequency setting. At the same time, this requirement complicates a\nfurther corrective filtering, because correction filter may become too steep\nat the point where the stop-band begins.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSecondly, speaking about non-2X-upsized resizing, filter has to be very short\n(with a base length of 5-7 taps, further multiplied by the resizing factor) or\notherwise the ringing artifacts will be very strong: it is a general rule that\nthe steeper the filter is around signal frequencies being removed the higher\nthe ringing artifacts are. That is why it is preferred to move steep\ntransitions into the spectral area with a quieter signal. A short filter also\nmeans it cannot provide a strong \"beyond-Nyquist\" stop-band attenuation, so an\ninterpolated image will look a bit edgy or not very clean due to stop-band\nartifacts.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo sum up, only additional controlled 2X upsizing provides enough spectral\nspace to design interpolation filter without visible ringing artifacts yet\nproviding a strong stop-band attenuation and stable spectral characteristics\n(good at any resizing \"k\" factor). Moreover, 2X upsizing becomes very\nimportant in maintaining a good resizing quality when downsizing and upsizing\nby small \"k\" factors, in the range 0.5 to 2: resizing approaches that do not\nperform 2X upsizing usually cannot design a good interpolation filter for such\nfactors just because there is not enough spectral space available.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhy Peaked Cosine in AVIR?\u003c/h2\u003e\u003ca id=\"user-content-why-peaked-cosine-in-avir\" class=\"anchor\" aria-label=\"Permalink: Why Peaked Cosine in AVIR?\" href=\"#why-peaked-cosine-in-avir\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFirst of all, AVIR is a general solution to image resizing problem. That is\nwhy it should not be directly compared to \"spline interpolation\" or \"Lanczos\nresampling\", because the latter two are only means to design interpolation\nfilters, and they can be implemented in a variety of ways, even in sub-optimal\nways. Secondly, with only a minimal effort AVIR can be changed to use any\nexisting interpolation formula and any window function, but this is just not\nneeded.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAn effort was made to compare Peaked Cosine to Lanczos window function, and\nhere is the author's opinion. Peaked Cosine has two degrees of freedom whereas\nLanczos has one degree of freedom. While both functions can be used with\nacceptable results, Peaked Cosine window function used in automatic parameter\noptimization really pushes the limits of frequency response linearity,\nanti-aliasing strength (stop-band attenuation) and low-ringing performance\nwhich Lanczos cannot usually achieve. This is true at least when using a\ngeneral-purpose downhill simplex optimization method. Lanczos window has good\n(but not better) characteristics in several special cases (certain \"k\"\nfactors) which makes it of limited use in a general solution such as AVIR.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAmong other window functions (Kaiser, Gaussian, Cauchy, Poisson, generalized\ncosine windows) there are no better candidates as well. It looks like Peaked\nCosine function's scalability (it retains stable, almost continously-variable\nspectral characteristics at any window parameter values), and its ability to\ncreate \"desirable\" pass-band ripple in the frequency response near the cutoff\npoint contribute to its better overall quality. Somehow Peaked Cosine window\nfunction optimization manages to converge to reasonable states in most cases\n(that is why AVIR library comes with a set of equally robust, but distinctive\nparameter sets) whereas all other window functions tend to produce\nunpredictable optimization results.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe only disadvantage of Peaked Cosine window function is that usable filters\nwindowed by this function tend to be longer than \"usual\" (with Kaiser window\nbeing the \"golden standard\" for filter length per decibel of stop-band\nattenuation). This is a price that should be paid for stable spectral\ncharacteristics.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis waterfall graph depicts the windowing function, at varying Alpha values.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/avaneev/avir/blob/master/other/_peaked_cosine.png\"\u003e\u003cimg src=\"/avaneev/avir/raw/master/other/_peaked_cosine.png\" width=\"550\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote that since mathematical formulas cannot be patented nor copyrighted, you\nare free to adopt this windowing function in your applications and research.\nJust consider giving it a proper credit.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLANCIR\u003c/h2\u003e\u003ca id=\"user-content-lancir\" class=\"anchor\" aria-label=\"Permalink: LANCIR\" href=\"#lancir\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs a part of AVIR library, the \u003ccode\u003eCLancIR\u003c/code\u003e class is also offered which is an\noptimal implementation of \u003ca href=\"https://en.wikipedia.org/wiki/Lanczos_resampling\" rel=\"nofollow\"\u003eLanczos\u003c/a\u003e\nimage resizing filter. This class has a similar programmatic interface to\nAVIR, but it is not thread-safe: each executing thread should have its own\n\u003ccode\u003eCLancIR\u003c/code\u003e object. This class was designed for cases of batch processing of\nsame-sized frames like in video encoding, or for just-in-time resizing of\nan application's assets. This Lanczos implementation is likely one of the\nfastest available for CPUs; it features radical AVX, SSE2, and NEON\noptimizations.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eLANCIR offers up to three times faster image resizing in comparison to AVIR.\nThe quality difference is, however, debatable. Note that while LANCIR can\ntake 8- and 16-bit and float image buffers, its precision is limited to\n8-bit resizing.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eLANCIR should be seen as a bonus and as an \"industrial standard\" reference\nfor comparison. By default, LANCIR uses Lanczos filter's \u003ccode\u003ea\u003c/code\u003e parameter equal\nto 3 which is similar to AVIR's default setting.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComparison\u003c/h2\u003e\u003ca id=\"user-content-comparison\" class=\"anchor\" aria-label=\"Permalink: Comparison\" href=\"#comparison\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis graph displays a comparison of AVIR 2.9 (default parameters) and\nLanczos-3 image resizing algorithm in the area of frequency response.\nThe methodology can be seen in the \u003ccode\u003eother/frtest.cpp\u003c/code\u003e file. This graph\ndisplays an average frequency response over a set of resizing factors.\nIt is similar but not equal to Fourier analysis as any errors and aliasing\nartifacts are integrated into the response. As you can see, AVIR offers a\nvisibly better frequency response linearity. The horizontal scale displays a\nnormalized frequency scale, where 0 is DC frequency and 1 is Nyquist\nfrequency. In common terms, 1 corresponds to 1-pixel image features, 0.5\ncorresponds to 2-pixel features while 0.25 corresponds to 4-pixel features,\netc. The vertical scale is in decibel.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/avaneev/avir/blob/master/other/_fr_up.png\"\u003e\u003cimg src=\"https://github.com/avaneev/avir/raw/master/other/_fr_up.png\" alt=\"FR plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe following graph displays a comparison of an average dynamic range over a\nset of resizing factors. The dynamic range is estimated by performing\ntwo-way resizing, followed by deviation/error estimation relative to the\noriginal image. As you can see here, aliasing artifacts visibly reduce dynamic\nrange above 0.5*Nyquist. An interesting aspect of this measurement method is\nthat it reflects modes of visual ringing very well: they correspond to the\npoints on frequency response where differential approaches zero.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/avaneev/avir/blob/master/other/_dr_up.png\"\u003e\u003cimg src=\"https://github.com/avaneev/avir/raw/master/other/_dr_up.png\" alt=\"DR plot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote that on downsizing the response graphs look similar to these.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsers\u003c/h2\u003e\u003ca id=\"user-content-users\" class=\"anchor\" aria-label=\"Permalink: Users\" href=\"#users\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis library is used by:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://www.contaware.com/\" rel=\"nofollow\"\u003eContaware.com\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/wtsi-hpag/PretextSnapshot\"\u003ePretext contact maps\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://lvcaudio.com\" rel=\"nofollow\"\u003eLVC Audio\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.trainzportal.com/files/TRS19/credits.html\" rel=\"nofollow\"\u003eTrainz\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://mlv.app/\" rel=\"nofollow\"\u003eMLV App\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://www.youtube.com/watch?v=oNF-c6YX7-8\" rel=\"nofollow\"\u003eThis video\u003c/a\u003e was \"unsqueezed\"\nwith AVIR by a factor of 3 from ML RAW video, and at the final stage was\ndownsampled to 4K resolution.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlease drop me a note at \u003ca href=\"mailto:aleksey.vaneev@gmail.com\"\u003ealeksey.vaneev@gmail.com\u003c/a\u003e and I will include a link to\nyour software product to the list of users. This list is important at\nmaintaining confidence in this library among the interested parties.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eChange Log\u003c/h2\u003e\u003ca id=\"user-content-change-log\" class=\"anchor\" aria-label=\"Permalink: Change Log\" href=\"#change-log\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eVersion 3.0:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImproved speed by 10-25% on upsizing by utilizing a special resizing\nfunction together with filter-less 2X upsizing. Does not apply to the\nde-interleaved (AVX) resizing.\u003c/li\u003e\n\u003cli\u003eMinor LANCIR optimization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.9:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRemoved a rarely-used half-band resizing step completely since it offers no\npractical performance nor quality benefits.\u003c/li\u003e\n\u003cli\u003eOptimized filter generation function (removed divisions by a constant) as\nfilters are always post-normalized anyway. This may reduce overhead when\ncreating thumbnail-sized images.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.8:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFixed regression with the copy-constructor of CImageResizeVars class\n(previously it caused uninitialized accesses).\u003c/li\u003e\n\u003cli\u003eRemoved filter length optimization as it did not reduce overhead measurably.\u003c/li\u003e\n\u003cli\u003eOptimized \"peaked cosine\" window function generator (removed division).\u003c/li\u003e\n\u003cli\u003eAdded \"unbiasing\" to resizer - an unconventional approach which reduces peak\nerror significantly, at the expense of 5% increased overhead.\u003c/li\u003e\n\u003cli\u003eReoptimized filter parameters, now yielding an unprecedented quality.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.7:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAdded normalization of individual fractional delay filters. This reduced\npeak error by 3 dB, which is substantial for image resizing.\u003c/li\u003e\n\u003cli\u003eReoptimized all filter parameters resulting in better frequency response\nlinearity.\u003c/li\u003e\n\u003cli\u003eAdded AVIR_NOCTOR macro to avoid copy-constructing and copying objects of\nsome classes via a default copy function.\u003c/li\u003e\n\u003cli\u003eAdded copy-constructor and assignment operator to the CImageResizerVars\nclass, to avoid uninitialized memory copying.\u003c/li\u003e\n\u003cli\u003eCorrected automatic image offseting and \"k\" factor on image upsizing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.6:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eA minor fix to sRGB gamma approximation functions.\u003c/li\u003e\n\u003cli\u003eLANCIR: fixed a rare access violation crash.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.5:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSurrounded \u003ccode\u003ememcpy\u003c/code\u003e calls with length checks to conform to \u003ccode\u003ememcpy\u003c/code\u003e\nspecification which does not allow NULL pointers even with zero copy length.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.4:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRemoved outdated \u003ccode\u003e_mm_reset()\u003c/code\u003e function calls from the SIMD code.\u003c/li\u003e\n\u003cli\u003eChanged \u003ccode\u003efloat4 round()\u003c/code\u003e to use SSE2 rounding features, avoiding use of\n64-bit registers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.3:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eImplemented CLancIR image resizing algorithm.\u003c/li\u003e\n\u003cli\u003eFixed a minor image offset on image upsizing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.2:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eReleased AVIR under a permissive MIT license agreement.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.1:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFixed error-diffusion dither problems introduced in the previous version.\u003c/li\u003e\n\u003cli\u003eAdded the \u003ccode\u003e-1\u003c/code\u003e switch to the \u003ccode\u003eimageresize\u003c/code\u003e to enable 1-bit output for\ndither's quality evaluation (use together with the \u003ccode\u003e-d\u003c/code\u003e switch).\u003c/li\u003e\n\u003cli\u003eAdded the \u003ccode\u003e--algparams=\u003c/code\u003e switch to the \u003ccode\u003eimageresize\u003c/code\u003e to control resizing\nquality (replaces the \u003ccode\u003e--low-ring\u003c/code\u003e switch).\u003c/li\u003e\n\u003cli\u003eAdded \u003ccode\u003eavir :: CImageResizerParamsULR\u003c/code\u003e parameter set for lowest-ringing\nperformance possible (not considerably different to\n\u003ccode\u003eavir :: CImageResizerParamsLR\u003c/code\u003e, but a bit lower ringing).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eVersion 2.0:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMinor inner loop optimizations.\u003c/li\u003e\n\u003cli\u003eLifted the supported image size constraint by switching buffer addressing to\n\u003ccode\u003esize_t\u003c/code\u003e from \u003ccode\u003eint\u003c/code\u003e, now image size is limited by the available system memory.\u003c/li\u003e\n\u003cli\u003eAdded several useful switches to the \u003ccode\u003eimageresize\u003c/code\u003e utility.\u003c/li\u003e\n\u003cli\u003eNow \u003ccode\u003eimageresize\u003c/code\u003e does not apply gamma-correction by default.\u003c/li\u003e\n\u003cli\u003eFixed scaling of bit depth-reduction operation.\u003c/li\u003e\n\u003cli\u003eImproved error-diffusion dither's signal-to-noise ratio.\u003c/li\u003e\n\u003cli\u003eCompiled binaries with AVX2 instruction set (SSE4 for macOS).\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"AVIR - Image Resizing Algorithm (in C++)","anchor":"avir---image-resizing-algorithm-in-c","htmlText":"AVIR - Image Resizing Algorithm (in C++)"},{"level":2,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":2,"text":"Affine and Non-Linear Transformations","anchor":"affine-and-non-linear-transformations","htmlText":"Affine and Non-Linear Transformations"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"Usage Information","anchor":"usage-information","htmlText":"Usage Information"},{"level":2,"text":"SIMD Usage Information","anchor":"simd-usage-information","htmlText":"SIMD Usage Information"},{"level":2,"text":"Notes","anchor":"notes","htmlText":"Notes"},{"level":2,"text":"Interpolation Discussion","anchor":"interpolation-discussion","htmlText":"Interpolation Discussion"},{"level":2,"text":"Why 2X upsizing in AVIR?","anchor":"why-2x-upsizing-in-avir","htmlText":"Why 2X upsizing in AVIR?"},{"level":2,"text":"Why Peaked Cosine in AVIR?","anchor":"why-peaked-cosine-in-avir","htmlText":"Why Peaked Cosine in AVIR?"},{"level":2,"text":"LANCIR","anchor":"lancir","htmlText":"LANCIR"},{"level":2,"text":"Comparison","anchor":"comparison","htmlText":"Comparison"},{"level":2,"text":"Users","anchor":"users","htmlText":"Users"},{"level":2,"text":"Change Log","anchor":"change-log","htmlText":"Change 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aria-describedby=":Rmlab:-loading-announcement"><svg aria-hidden="true" focusable="false" class="octicon octicon-tag" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path></svg></a></div></div><div class="Box-sc-g0xbh4-0 jxTzTd"><div class="Box-sc-g0xbh4-0 gqqBXN"><div class="Box-sc-g0xbh4-0 dzXgxt"><!--$--><div class="Box-sc-g0xbh4-0 iWFGlI"><span class="Box-sc-g0xbh4-0 vcvyP TextInput-wrapper prc-components-TextInputWrapper-i1ofR prc-components-TextInputBaseWrapper-ueK9q" data-leading-visual="true" data-trailing-visual="true" aria-busy="false"><span class="TextInput-icon" id=":R2j5ab:" aria-hidden="true"><svg aria-hidden="true" focusable="false" class="octicon octicon-search" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path></svg></span><input type="text" aria-label="Go to file" role="combobox" aria-controls="file-results-list" aria-expanded="false" aria-haspopup="dialog" autoCorrect="off" spellcheck="false" placeholder="Go to file" aria-describedby=":R2j5ab: :R2j5abH1:" data-component="input" class="prc-components-Input-Ic-y8" value=""/><span class="TextInput-icon" id=":R2j5abH1:" aria-hidden="true"></span></span></div><!--/$--></div><div class="Box-sc-g0xbh4-0 YUPas"><button type="button" class="prc-Button-ButtonBase-c50BI" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="default" aria-describedby=":Rr5ab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="text" class="prc-Button-Label-pTQ3x">Go to file</span></span></button></div><div class="react-directory-add-file-icon"></div><div class="react-directory-remove-file-icon"></div></div><button type="button" aria-haspopup="true" aria-expanded="false" tabindex="0" class="prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="primary" aria-describedby=":R55ab:-loading-announcement" id=":R55ab:"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-code hide-sm" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div class="Box-sc-g0xbh4-0 izFOf"><button data-component="IconButton" type="button" aria-label="Open more actions menu" aria-haspopup="true" aria-expanded="false" tabindex="0" class="prc-Button-ButtonBase-c50BI prc-Button-IconButton-szpyj" data-loading="false" data-no-visuals="true" data-size="medium" data-variant="default" aria-describedby=":R75ab:-loading-announcement" id=":R75ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-kebab-horizontal" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div><div class="Box-sc-g0xbh4-0 vIPPs"><div data-hpc="true"><button hidden="" data-testid="focus-next-element-button" data-hotkey="j"></button><button hidden="" data-testid="focus-previous-element-button" data-hotkey="k"></button><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only ScreenReaderHeading-module__userSelectNone--vW4Cq prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/avaneev/avir/commits/master/" class="prc-Button-ButtonBase-c50BI d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">103 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="103 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a href="/avaneev/avir/commits/master/" class="prc-Button-ButtonBase-c50BI LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" data-variant="invisible" aria-describedby=":R1iqj8pab:-loading-announcement history-icon-button-tooltip"><span data-component="buttonContent" data-align="center" class="prc-Button-ButtonContent-HKbr-"><span data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 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id="folder-row-1"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file-directory-fill icon-directory" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M1.75 1A1.75 1.75 0 0 0 0 2.75v10.5C0 14.216.784 15 1.75 15h12.5A1.75 1.75 0 0 0 16 13.25v-8.5A1.75 1.75 0 0 0 14.25 3H7.5a.25.25 0 0 1-.2-.1l-.9-1.2C6.07 1.26 5.55 1 5 1H1.75Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="other" aria-label="other, (Directory)" class="Link--primary" href="/avaneev/avir/tree/master/other">other</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon 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focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div class="react-directory-truncate"><a title="LICENSE" aria-label="LICENSE, (File)" class="Link--primary" href="/avaneev/avir/blob/master/LICENSE">LICENSE</a></div></div></div></div></td><td class="react-directory-row-name-cell-large-screen" colSpan="1"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" 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octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Introduction</h2><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Me, Aleksey Vaneev, is happy to offer you an open source image resizing / scaling library which has reached a production level of quality, and is ready to be incorporated into any project. This library features routines for both down- and upsizing of 8- and 16-bit, 1 to 4-channel images. Image resizing routines were implemented in a portable, cross-platform, header-only C++ code, and have a high level of optimality. Beside resizing, this library offers a sub-pixel shift operation. Built-in sRGB gamma correction is available.</p> <p dir="auto">The resizing algorithm at first produces 2X upsized image (relative to the source image size, or relative to the destination image size if downsizing is performed) and then performs interpolation using a bank of sinc function-based fractional delay filters. At the last stage a correction filter is applied which fixes smoothing introduced at previous steps.</p> <p dir="auto">The resizing algorithm was designed to provide the best visual quality. The author even believes this algorithm provides the "ultimate" level of quality (for an orthogonal, non neural-network, resizing) which cannot be increased further: no math exists to provide a better frequency response, better anti-aliasing quality and at the same time having less ringing artifacts: these are 3 elements that define any resizing algorithm's quality; in AVIR practice these elements have a high correlation to each other, so they can be represented by a single parameter (AVIR offers several parameter sets with varying quality). Algorithm's time performance turned out to be very good as well (for the "ultimate" image quality).</p> <p dir="auto">An important element utilized by this algorithm is the so called Peaked Cosine window function, which is applied over sinc function in all filters. Please consult the documentation for more details.</p> <p dir="auto">Note that since AVIR implements orthogonal resizing, it may exhibit diagonal aliasing artifacts. These artifacts are usually suppressed by EWA or radial filtering techniques. EWA-like technique is not implemented in AVIR, because it requires considerably more computing resources and may produce a blurred image.</p> <p dir="auto">As a bonus, a much faster <code>LANCIR</code> image resizing algorithm is also offered as a part of this library. But the main focus of this documentation is the original AVIR image resizing algorithm.</p> <p dir="auto"><em>AVIR is dedicated to women. Your digital photos can look good at any size!</em></p> <p dir="auto">P.S. Please credit the author of this library in your documentation in the following way: "AVIR image resizing algorithm designed by Aleksey Vaneev".</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Affine and Non-Linear Transformations</h2><a id="user-content-affine-and-non-linear-transformations" class="anchor" aria-label="Permalink: Affine and Non-Linear Transformations" href="#affine-and-non-linear-transformations"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">AVIR does not offer affine and non-linear image transformations "out of the box". Since upsizing is a relatively fast operation in AVIR (required time scales linearly with the output image area), affine and non-linear transformations can be implemented in steps: 4- to 8-times upsizing, transformation via bilinear interpolation, downsizing (linear proportional affine transformations can probably skip the downsizing step). This should not compromise the transformation quality much as bilinear interpolation's problems will mostly reside in spectral area without useful signal, with a maximum of 0.7 dB high-frequency attenuation for 4-times upsizing, and 0.17 dB attenuation for 8-times upsizing. This approach is probably as time efficient as performing a high-quality transform over the input image directly (the only serious drawback is the increased memory requirement). Note that affine transformations that change image proportions should first apply proportion change during upsizing.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">C++ compiler and system with efficient "float" floating-point (24-bit mantissa) type support. This library can also internally use the "double" and SIMD floating-point types during resizing if needed. This library does not have dependencies beside the standard C library.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage Information</h2><a id="user-content-usage-information" class="anchor" aria-label="Permalink: Usage Information" href="#usage-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The image resizer is represented by the <code>avir::CImageResizer<></code> class, which is a single front-end class for the whole library. Basically, you do not need to use nor understand any other classes beside this class.</p> <ul dir="auto"> <li><a href="https://www.voxengo.com/public/avir/Documentation/" rel="nofollow">Documentation</a></li> </ul> <p dir="auto">The code of the library resides in the "avir" C++ namespace, effectively isolating it from all other code. The code is thread-safe. You need just a single resizer object per running application, at any time, even when resizing images concurrently.</p> <p dir="auto">To resize images in your application, simply add 3 lines of code (note that you may need to change <code>ImageResizer( 8 )</code> here, to specify your image's true bit resolution, which may be 10 or even 16):</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#include "avir.h" avir :: CImageResizer<> ImageResizer( 8 ); ImageResizer.resizeImage( InBuf, 640, 480, 0, OutBuf, 1024, 768, 3, 0 ); (multi-threaded operation requires additional coding, see the documentation)"><pre>#<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds">"</span>avir.h<span class="pl-pds">"</span></span> avir :: CImageResizer<> <span class="pl-en">ImageResizer</span>( <span class="pl-c1">8</span> ); ImageResizer.resizeImage( InBuf, <span class="pl-c1">640</span>, <span class="pl-c1">480</span>, <span class="pl-c1">0</span>, OutBuf, <span class="pl-c1">1024</span>, <span class="pl-c1">768</span>, <span class="pl-c1">3</span>, <span class="pl-c1">0</span> ); (multi-threaded operation requires additional coding, see the documentation)</pre></div> <p dir="auto">AVIR works with header-less "raw" image buffers. If you are not too familiar with the low-level "packed interleaved" image storage format, the <code>InBuf</code> is expected to be <code>w*h*c</code> elements in size, where <code>w</code> and <code>h</code> is the width and the height of the image in pixels, respectively, and <code>c</code> is the number of color channels in the image. In the example above, the size of the <code>InBuf</code> is <code>640*480*3=921600</code> elements. If you are working with 8-bit images, the buffer and the elements should have the <code>uint8_t*</code> type; if you are working with 16-bit images, they should have the <code>uint16_t*</code> type. Note that when processing 16-bit images, the value of <code>16</code> should be used in resizer's constructor. AVIR's algorithm does not discern between channel packing order (<code>RGBA</code>, <code>ARGB</code>, <code>BGRA</code>, etc.), so if the <code>BGRA</code> ordered elements were passed to it, the result will be also <code>BGRA</code>.</p> <p dir="auto">If the graphics library you are using returns a <code>uint32_t*</code> pointer to a raw 4-channel packed pixel data, you will need to cast both the input and output pointers to the <code>uint8_t*</code> type when supplying them to the resizing function, and set the ElCount to 4.</p> <p dir="auto">For low-ringing performance:</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="avir :: CImageResizer<> ImageResizer( 8, 0, avir :: CImageResizerParamsLR() );"><pre>avir :: CImageResizer<> <span class="pl-en">ImageResizer</span>( <span class="pl-c1">8</span>, <span class="pl-c1">0</span>, avir :: CImageResizerParamsLR() );</pre></div> <p dir="auto">To use the built-in gamma correction, which is disabled by default, an object of the <code>avir::CImageResizerVars</code> class with its variable <code>UseSRGBGamma</code> set to <code>true</code> should be supplied to the <code>resizeImage()</code> function. Note that, when enabled, the gamma correction is applied to all channels (e.g. alpha-channel) in the current implementation.</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="avir :: CImageResizerVars Vars; Vars.UseSRGBGamma = true;"><pre>avir :: CImageResizerVars Vars; Vars.UseSRGBGamma = <span class="pl-c1">true</span>;</pre></div> <p dir="auto">Dithering (error-diffusion dither which is perceptually good) can be enabled this way:</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="typedef avir :: fpclass_def< float, float, avir :: CImageResizerDithererErrdINL< float > > fpclass_dith; avir :: CImageResizer< fpclass_dith > ImageResizer( 8 );"><pre><span class="pl-k">typedef</span> avir :: fpclass_def< <span class="pl-k">float</span>, <span class="pl-k">float</span>, avir :: CImageResizerDithererErrdINL< <span class="pl-k">float</span> > > fpclass_dith; avir :: CImageResizer< fpclass_dith > <span class="pl-en">ImageResizer</span>( <span class="pl-c1">8</span> );</pre></div> <p dir="auto">The library is able to process images of any bit depth: this includes 8-bit, 16-bit, float and double types. Larger integer and signed integer types are not supported. Supported source and destination image sizes are only limited by the available system memory. Note that the resizing function applies clipping to integer output only; floating-point output will not be clipped to [0; 1] range.</p> <p dir="auto">The code of this library was commented in the <a href="http://www.doxygen.org/" rel="nofollow">Doxygen</a> style. To generate the documentation locally you may run the <code>doxygen ./other/avirdoxy.txt</code> command from the library's directory. Note that the code was suitably documented allowing you to make modifications, and to gain full understanding of the algorithm.</p> <p dir="auto">Preliminary tests show that this library (compiled with Intel C++ Compiler 18.2 with AVX2 instructions enabled, without explicit SIMD resizing code) can resize 8-bit RGB 5184x3456 (17.9 Mpixel) 3-channel image down to 1920x1280 (2.5 Mpixel) image in 245 milliseconds, utilizing a single thread, on Intel Core i7-7700K processor-based system without overclocking. This scales down to 74 milliseconds if 8 threads are utilized.</p> <p dir="auto">Multi-threaded operation is not provided by this library "out of the box". The multi-threaded (horizontally-threaded) infrastructure is available, but requires additional system-specific interfacing code for engagement.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">SIMD Usage Information</h2><a id="user-content-simd-usage-information" class="anchor" aria-label="Permalink: SIMD Usage Information" href="#simd-usage-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This library is capable of using SIMD floating-point types for internal variables. This means that up to 4 color channels can be processed in parallel. Since the default interleaved processing algorithm itself remains non-SIMD, the use of SIMD internal types is not practical for 1- and 2-channel image resizing (due to overhead). SIMD internal type can be used this way:</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#include "avir_float4_sse.h" avir :: CImageResizer< avir :: fpclass_float4 > ImageResizer( 8 );"><pre>#<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds">"</span>avir_float4_sse.h<span class="pl-pds">"</span></span> avir :: CImageResizer< avir :: fpclass_float4 > <span class="pl-en">ImageResizer</span>( <span class="pl-c1">8</span> );</pre></div> <p dir="auto">For 1-channel and 2-channel image resizing when AVX instructions are allowed it may be reasonable to utilize de-interleaved SIMD processing algorithm. While it gives no performance benefit if the "float4" SSE processing type is used, it offers some performance boost if the "float8" AVX processing type is used (given dithering is not performed, or otherwise performance is reduced at the dithering stage since recursive dithering cannot be parallelized). The internal type remains non-SIMD "float". De-interleaved algorithm can be used this way:</p> <div class="highlight highlight-source-c++ notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="#include "avir_float8_avx.h" avir :: CImageResizer< avir :: fpclass_float8_dil > ImageResizer( 8 );"><pre>#<span class="pl-k">include</span> <span class="pl-s"><span class="pl-pds">"</span>avir_float8_avx.h<span class="pl-pds">"</span></span> avir :: CImageResizer< avir :: fpclass_float8_dil > <span class="pl-en">ImageResizer</span>( <span class="pl-c1">8</span> );</pre></div> <p dir="auto">It's important to note that on the latest Intel processors (i7-7700K and probably later) the use of the aforementioned SIMD-specific resizing code may not be justifiable, or may be even counter-productive due to many factors: memory bandwidth bottleneck, increased efficiency of processor's circuitry utilization and out-of-order execution, automatic SIMD optimizations performed by the compiler. This is at least true when compiling 64-bit code with Intel C++ Compiler 18.2 with /QxSSE4.2, or especially with the /QxCORE-AVX2 option. SSE-specific resizing code may still be a little bit more efficient for 4-channel image resizing.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Notes</h2><a id="user-content-notes" class="anchor" aria-label="Permalink: Notes" href="#notes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This library was tested for compatibility with <a href="http://gcc.gnu.org/" rel="nofollow">GNU C++</a>, <a href="http://www.microsoft.com/visualstudio/eng/products/visual-studio-express-products" rel="nofollow">Microsoft Visual C++</a>, <a href="https://llvm.org/" rel="nofollow">LLVM</a>, and <a href="http://software.intel.com/en-us/c-compilers" rel="nofollow">Intel C++</a> compilers, on 32- and 64-bit Windows, macOS, and CentOS Linux. The code was also tested with Dr.Memory/Win32 for the absence of uninitialized or unaddressable memory accesses.</p> <p dir="auto">All code is fully "inline", without the need to compile any source files. The memory footprint of the library itself is very modest, except that the size of the temporary image buffers depends on the input and output image sizes, and is proportionally large.</p> <p dir="auto">The "heart" of resizing algorithm's quality resides in the parameters defined via the <code>avir::CImageResizerParams</code> structure. While the default set of parameters that offers a good quality was already provided, there is (probably) still a place for improvement exists, and the default parameters may change in a future update. If you need to recall an exact set of parameters, simply save them locally for a later use.</p> <p dir="auto">When the algorithm is run with no resizing applied (k=1), the result of resizing will not be an exact, but a very close copy of the source image. The reason for such inexactness is that the image is always low-pass filtered at first to reduce aliasing during subsequent resizing, and at last filtered by a correction filter. Such approach allows algorithm to maintain a stable level of quality regardless of the resizing "k" factor used.</p> <p dir="auto">This library includes a binary command line tool "imageresize" for some desktop platforms. This tool was designed to be used as a demonstration of library's performance, and as a reference, it is multi-threaded (the <code>-t</code> switch can be used to control the number of threads utilized). This tool uses plain "float" processing (no explicit SIMD) and relies on automatic compiler optimizations. This tool uses the following libraries:</p> <ul dir="auto"> <li>turbojpeg Copyright (c) 2009-2013 D. R. Commander</li> <li>libpng Copyright (c) 1998-2013 Glenn Randers-Pehrson</li> <li>zlib Copyright (c) 1995-2013 Jean-loup Gailly and Mark Adler</li> </ul> <p dir="auto">Note that you can enable gamma-correction with the <code>-g</code> switch. However, sometimes gamma-correction produces "greenish/reddish/bluish haze" since low-amplitude oscillations produced by resizing at object boundaries are amplified by gamma correction. This can also have an effect of reduced contrast.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Interpolation Discussion</h2><a id="user-content-interpolation-discussion" class="anchor" aria-label="Permalink: Interpolation Discussion" href="#interpolation-discussion"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The use of certain low-pass filters and 2X upsampling in this library is hardly debatable, because they are needed to attain a certain anti-aliasing effect and keep ringing artifacts low. But the use of sinc function-based interpolation filter that is 18 taps-long (may be higher, up to 36 taps in practice) can be questioned, because such interpolation filter requires 18 multiply-add operations. Comparatively, an optimal Hermite or cubic interpolation spline requires 8 multiply and 11 add operations.</p> <p dir="auto">One of the reasons 18-tap filter is preferred, is because due to memory bandwidth limitations using a lower-order filter does not provide any significant performance increase (e.g. 14-tap filter is less than 5% more efficient overall). At the same time, in comparison to cubic spline, 18-tap filter embeds a low-pass filter that rejects signal above 0.5*pi (provides additional anti-aliasing filtering), and this filter has a consistent shape at all fractional offsets. Splines have a varying low-pass filter shape at different fractional offsets (e.g. no low-pass filtering at 0.0 offset, and maximal low-pass filtering at 0.5 offset). 18-tap filter also offers a superior stop-band attenuation which almost guarantees absence of artifacts if the image is considerably sharpened afterwards.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why 2X upsizing in AVIR?</h2><a id="user-content-why-2x-upsizing-in-avir" class="anchor" aria-label="Permalink: Why 2X upsizing in AVIR?" href="#why-2x-upsizing-in-avir"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Classic approaches to image resizing do not perform an additional 2X upsizing. So, why such upsizing is needed at all in AVIR? Indeed, image resizing can be implemented using a single interpolation filter which is applied to the source image directly. However, such approach has limitations:</p> <p dir="auto">First of all, speaking about non-2X-upsized resizing, during upsizing the interpolation filter has to be tuned to a frequency close to pi (Nyquist) in order to reduce high-frequency smoothing: this reduces the space left for filter optimization. Beside that, during downsizing, a filter that performs well and predictable when tuned to frequencies close to the Nyquist frequency, may become distorted in its spectral shape when it is tuned to lower frequencies. That is why it is usually a good idea to have filter's stop-band begin below Nyquist so that the transition band's shape remains stable at any lower-frequency setting. At the same time, this requirement complicates a further corrective filtering, because correction filter may become too steep at the point where the stop-band begins.</p> <p dir="auto">Secondly, speaking about non-2X-upsized resizing, filter has to be very short (with a base length of 5-7 taps, further multiplied by the resizing factor) or otherwise the ringing artifacts will be very strong: it is a general rule that the steeper the filter is around signal frequencies being removed the higher the ringing artifacts are. That is why it is preferred to move steep transitions into the spectral area with a quieter signal. A short filter also means it cannot provide a strong "beyond-Nyquist" stop-band attenuation, so an interpolated image will look a bit edgy or not very clean due to stop-band artifacts.</p> <p dir="auto">To sum up, only additional controlled 2X upsizing provides enough spectral space to design interpolation filter without visible ringing artifacts yet providing a strong stop-band attenuation and stable spectral characteristics (good at any resizing "k" factor). Moreover, 2X upsizing becomes very important in maintaining a good resizing quality when downsizing and upsizing by small "k" factors, in the range 0.5 to 2: resizing approaches that do not perform 2X upsizing usually cannot design a good interpolation filter for such factors just because there is not enough spectral space available.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Why Peaked Cosine in AVIR?</h2><a id="user-content-why-peaked-cosine-in-avir" class="anchor" aria-label="Permalink: Why Peaked Cosine in AVIR?" href="#why-peaked-cosine-in-avir"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">First of all, AVIR is a general solution to image resizing problem. That is why it should not be directly compared to "spline interpolation" or "Lanczos resampling", because the latter two are only means to design interpolation filters, and they can be implemented in a variety of ways, even in sub-optimal ways. Secondly, with only a minimal effort AVIR can be changed to use any existing interpolation formula and any window function, but this is just not needed.</p> <p dir="auto">An effort was made to compare Peaked Cosine to Lanczos window function, and here is the author's opinion. Peaked Cosine has two degrees of freedom whereas Lanczos has one degree of freedom. While both functions can be used with acceptable results, Peaked Cosine window function used in automatic parameter optimization really pushes the limits of frequency response linearity, anti-aliasing strength (stop-band attenuation) and low-ringing performance which Lanczos cannot usually achieve. This is true at least when using a general-purpose downhill simplex optimization method. Lanczos window has good (but not better) characteristics in several special cases (certain "k" factors) which makes it of limited use in a general solution such as AVIR.</p> <p dir="auto">Among other window functions (Kaiser, Gaussian, Cauchy, Poisson, generalized cosine windows) there are no better candidates as well. It looks like Peaked Cosine function's scalability (it retains stable, almost continously-variable spectral characteristics at any window parameter values), and its ability to create "desirable" pass-band ripple in the frequency response near the cutoff point contribute to its better overall quality. Somehow Peaked Cosine window function optimization manages to converge to reasonable states in most cases (that is why AVIR library comes with a set of equally robust, but distinctive parameter sets) whereas all other window functions tend to produce unpredictable optimization results.</p> <p dir="auto">The only disadvantage of Peaked Cosine window function is that usable filters windowed by this function tend to be longer than "usual" (with Kaiser window being the "golden standard" for filter length per decibel of stop-band attenuation). This is a price that should be paid for stable spectral characteristics.</p> <p dir="auto">This waterfall graph depicts the windowing function, at varying Alpha values.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/avaneev/avir/blob/master/other/_peaked_cosine.png"><img src="/avaneev/avir/raw/master/other/_peaked_cosine.png" width="550" style="max-width: 100%;"></a></p> <p dir="auto">Note that since mathematical formulas cannot be patented nor copyrighted, you are free to adopt this windowing function in your applications and research. Just consider giving it a proper credit.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">LANCIR</h2><a id="user-content-lancir" class="anchor" aria-label="Permalink: LANCIR" href="#lancir"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As a part of AVIR library, the <code>CLancIR</code> class is also offered which is an optimal implementation of <a href="https://en.wikipedia.org/wiki/Lanczos_resampling" rel="nofollow">Lanczos</a> image resizing filter. This class has a similar programmatic interface to AVIR, but it is not thread-safe: each executing thread should have its own <code>CLancIR</code> object. This class was designed for cases of batch processing of same-sized frames like in video encoding, or for just-in-time resizing of an application's assets. This Lanczos implementation is likely one of the fastest available for CPUs; it features radical AVX, SSE2, and NEON optimizations.</p> <p dir="auto">LANCIR offers up to three times faster image resizing in comparison to AVIR. The quality difference is, however, debatable. Note that while LANCIR can take 8- and 16-bit and float image buffers, its precision is limited to 8-bit resizing.</p> <p dir="auto">LANCIR should be seen as a bonus and as an "industrial standard" reference for comparison. By default, LANCIR uses Lanczos filter's <code>a</code> parameter equal to 3 which is similar to AVIR's default setting.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Comparison</h2><a id="user-content-comparison" class="anchor" aria-label="Permalink: Comparison" href="#comparison"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This graph displays a comparison of AVIR 2.9 (default parameters) and Lanczos-3 image resizing algorithm in the area of frequency response. The methodology can be seen in the <code>other/frtest.cpp</code> file. This graph displays an average frequency response over a set of resizing factors. It is similar but not equal to Fourier analysis as any errors and aliasing artifacts are integrated into the response. As you can see, AVIR offers a visibly better frequency response linearity. The horizontal scale displays a normalized frequency scale, where 0 is DC frequency and 1 is Nyquist frequency. In common terms, 1 corresponds to 1-pixel image features, 0.5 corresponds to 2-pixel features while 0.25 corresponds to 4-pixel features, etc. The vertical scale is in decibel.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/avaneev/avir/blob/master/other/_fr_up.png"><img src="https://github.com/avaneev/avir/raw/master/other/_fr_up.png" alt="FR plot" style="max-width: 100%;"></a></p> <p dir="auto">The following graph displays a comparison of an average dynamic range over a set of resizing factors. The dynamic range is estimated by performing two-way resizing, followed by deviation/error estimation relative to the original image. As you can see here, aliasing artifacts visibly reduce dynamic range above 0.5*Nyquist. An interesting aspect of this measurement method is that it reflects modes of visual ringing very well: they correspond to the points on frequency response where differential approaches zero.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/avaneev/avir/blob/master/other/_dr_up.png"><img src="https://github.com/avaneev/avir/raw/master/other/_dr_up.png" alt="DR plot" style="max-width: 100%;"></a></p> <p dir="auto">Note that on downsizing the response graphs look similar to these.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Users</h2><a id="user-content-users" class="anchor" aria-label="Permalink: Users" href="#users"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This library is used by:</p> <ul dir="auto"> <li><a href="http://www.contaware.com/" rel="nofollow">Contaware.com</a></li> <li><a href="https://github.com/wtsi-hpag/PretextSnapshot">Pretext contact maps</a></li> <li><a href="https://lvcaudio.com" rel="nofollow">LVC Audio</a></li> <li><a href="https://www.trainzportal.com/files/TRS19/credits.html" rel="nofollow">Trainz</a></li> <li><a href="https://mlv.app/" rel="nofollow">MLV App</a></li> </ul> <p dir="auto"><a href="https://www.youtube.com/watch?v=oNF-c6YX7-8" rel="nofollow">This video</a> was "unsqueezed" with AVIR by a factor of 3 from ML RAW video, and at the final stage was downsampled to 4K resolution.</p> <p dir="auto">Please drop me a note at <a href="mailto:aleksey.vaneev@gmail.com">aleksey.vaneev@gmail.com</a> and I will include a link to your software product to the list of users. This list is important at maintaining confidence in this library among the interested parties.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Change Log</h2><a id="user-content-change-log" class="anchor" aria-label="Permalink: Change Log" href="#change-log"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Version 3.0:</p> <ul dir="auto"> <li>Improved speed by 10-25% on upsizing by utilizing a special resizing function together with filter-less 2X upsizing. Does not apply to the de-interleaved (AVX) resizing.</li> <li>Minor LANCIR optimization.</li> </ul> <p dir="auto">Version 2.9:</p> <ul dir="auto"> <li>Removed a rarely-used half-band resizing step completely since it offers no practical performance nor quality benefits.</li> <li>Optimized filter generation function (removed divisions by a constant) as filters are always post-normalized anyway. This may reduce overhead when creating thumbnail-sized images.</li> </ul> <p dir="auto">Version 2.8:</p> <ul dir="auto"> <li>Fixed regression with the copy-constructor of CImageResizeVars class (previously it caused uninitialized accesses).</li> <li>Removed filter length optimization as it did not reduce overhead measurably.</li> <li>Optimized "peaked cosine" window function generator (removed division).</li> <li>Added "unbiasing" to resizer - an unconventional approach which reduces peak error significantly, at the expense of 5% increased overhead.</li> <li>Reoptimized filter parameters, now yielding an unprecedented quality.</li> </ul> <p dir="auto">Version 2.7:</p> <ul dir="auto"> <li>Added normalization of individual fractional delay filters. This reduced peak error by 3 dB, which is substantial for image resizing.</li> <li>Reoptimized all filter parameters resulting in better frequency response linearity.</li> <li>Added AVIR_NOCTOR macro to avoid copy-constructing and copying objects of some classes via a default copy function.</li> <li>Added copy-constructor and assignment operator to the CImageResizerVars class, to avoid uninitialized memory copying.</li> <li>Corrected automatic image offseting and "k" factor on image upsizing.</li> </ul> <p dir="auto">Version 2.6:</p> <ul dir="auto"> <li>A minor fix to sRGB gamma approximation functions.</li> <li>LANCIR: fixed a rare access violation crash.</li> </ul> <p dir="auto">Version 2.5:</p> <ul dir="auto"> <li>Surrounded <code>memcpy</code> calls with length checks to conform to <code>memcpy</code> specification which does not allow NULL pointers even with zero copy length.</li> </ul> <p dir="auto">Version 2.4:</p> <ul dir="auto"> <li>Removed outdated <code>_mm_reset()</code> function calls from the SIMD code.</li> <li>Changed <code>float4 round()</code> to use SSE2 rounding features, avoiding use of 64-bit registers.</li> </ul> <p dir="auto">Version 2.3:</p> <ul dir="auto"> <li>Implemented CLancIR image resizing algorithm.</li> <li>Fixed a minor image offset on image upsizing.</li> </ul> <p dir="auto">Version 2.2:</p> <ul dir="auto"> <li>Released AVIR under a permissive MIT license agreement.</li> </ul> <p dir="auto">Version 2.1:</p> <ul dir="auto"> <li>Fixed error-diffusion dither problems introduced in the previous version.</li> <li>Added the <code>-1</code> switch to the <code>imageresize</code> to enable 1-bit output for dither's quality evaluation (use together with the <code>-d</code> switch).</li> <li>Added the <code>--algparams=</code> switch to the <code>imageresize</code> to control resizing quality (replaces the <code>--low-ring</code> switch).</li> <li>Added <code>avir :: CImageResizerParamsULR</code> parameter set for lowest-ringing performance possible (not considerably different to <code>avir :: CImageResizerParamsLR</code>, but a bit lower ringing).</li> </ul> <p dir="auto">Version 2.0:</p> <ul dir="auto"> <li>Minor inner loop optimizations.</li> <li>Lifted the supported image size constraint by switching buffer addressing to <code>size_t</code> from <code>int</code>, now image size is limited by the available system memory.</li> <li>Added several useful switches to the <code>imageresize</code> utility.</li> <li>Now <code>imageresize</code> does not apply gamma-correction by default.</li> <li>Fixed scaling of bit depth-reduction operation.</li> <li>Improved error-diffusion dither's signal-to-noise ratio.</li> <li>Compiled binaries with AVX2 instruction set (SSE4 for macOS).</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="vNpvMOag3OzavyIOxA/8R0kbyRlb15cWALJo8oK9B/sm/3fJYk456z+nEpY9OYCfJzCqFag9l7gbMdqGKDfHeg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> High-quality pro HDR image resizing / scaling C++ library, including a very fast, precise, SIMD Lanczos resizer (header-only C++) </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/bitmap" title="Topic: bitmap" data-view-component="true" class="topic-tag topic-tag-link"> bitmap </a> <a href="/topics/image-processing" title="Topic: image-processing" data-view-component="true" class="topic-tag topic-tag-link"> image-processing </a> <a href="/topics/image-manipulation" title="Topic: image-manipulation" data-view-component="true" class="topic-tag topic-tag-link"> image-manipulation </a> <a href="/topics/resampling" title="Topic: resampling" data-view-component="true" class="topic-tag topic-tag-link"> resampling </a> <a href="/topics/image-resizer" title="Topic: image-resizer" data-view-component="true" class="topic-tag topic-tag-link"> image-resizer </a> <a href="/topics/resize-images" title="Topic: resize-images" data-view-component="true" class="topic-tag topic-tag-link"> resize-images </a> <a href="/topics/image-scaling" title="Topic: image-scaling" data-view-component="true" class="topic-tag topic-tag-link"> image-scaling </a> <a href="/topics/upscaling" title="Topic: upscaling" data-view-component="true" class="topic-tag topic-tag-link"> upscaling </a> <a href="/topics/lanczos" title="Topic: lanczos" data-view-component="true" class="topic-tag topic-tag-link"> lanczos </a> <a href="/topics/resample" title="Topic: resample" data-view-component="true" class="topic-tag topic-tag-link"> resample </a> <a href="/topics/hdr-image" title="Topic: hdr-image" data-view-component="true" class="topic-tag topic-tag-link"> hdr-image </a> <a href="/topics/image-resolution" title="Topic: image-resolution" data-view-component="true" class="topic-tag topic-tag-link"> image-resolution </a> <a href="/topics/upscaler" title="Topic: upscaler" data-view-component="true" class="topic-tag topic-tag-link"> upscaler </a> <a href="/topics/resizer-image" title="Topic: resizer-image" data-view-component="true" class="topic-tag topic-tag-link"> resizer-image </a> <a href="/topics/image-upscaling" title="Topic: image-upscaling" data-view-component="true" class="topic-tag topic-tag-link"> image-upscaling </a> <a href="/topics/image-upsizing" title="Topic: image-upsizing" data-view-component="true" class="topic-tag topic-tag-link"> image-upsizing </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" 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href="/avaneev/avir/stargazers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>433</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/avaneev/avir/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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