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image compression</title> <meta name="description" content="Search results for: fractal image compression"> <meta name="keywords" content="fractal image compression"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="fractal image compression"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3708</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: fractal image compression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3708</span> GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Enamul%20Haque">Md. Enamul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Al%20Kaisan"> Abdullah Al Kaisan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmudur%20R.%20Saniat"> Mahmudur R. Saniat</a>, <a href="https://publications.waset.org/abstracts/search?q=Aminur%20Rahman"> Aminur Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There is several improvements in the implementation of the algorithm as well. Fractal image compression is based on the self similarity of an image, meaning an image having similarity in majority of the regions. We take this opportunity to implement the compression algorithm and monitor the effect of it using both parallel and sequential implementation. Fractal compression has the property of high compression rate and the dimensionless scheme. Compression scheme for fractal image is of two kinds, one is encoding and another is decoding. Encoding is very much computational expensive. On the other hand decoding is less computational. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification an inseparable feature of the fractal compression would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding process, which can last even several hours, is another bothersome drawback of the fractal compression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accelerated%20GPU" title="accelerated GPU">accelerated GPU</a>, <a href="https://publications.waset.org/abstracts/search?q=CUDA" title=" CUDA"> CUDA</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20computing" title=" parallel computing"> parallel computing</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20image%20compression" title=" fractal image compression"> fractal image compression</a> </p> <a href="https://publications.waset.org/abstracts/5645/gpu-accelerated-fractal-image-compression-for-medical-imaging-in-parallel-computing-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5645.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">336</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3707</span> Speeding-up Gray-Scale FIC by Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eman%20A.%20Al-Hilo">Eman A. Al-Hilo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawraa%20H.%20Al-Waelly"> Hawraa H. Al-Waelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20gray%20level%20image" title="fractal gray level image">fractal gray level image</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20compression%20technique" title=" fractal compression technique"> fractal compression technique</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20function%20system" title=" iterated function system"> iterated function system</a>, <a href="https://publications.waset.org/abstracts/search?q=moments%20feature" title=" moments feature"> moments feature</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-mean%20range-domain%20block" title=" zero-mean range-domain block"> zero-mean range-domain block</a> </p> <a href="https://publications.waset.org/abstracts/19903/speeding-up-gray-scale-fic-by-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19903.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3706</span> Lacunarity measures on Mammographic Image Applying Fractal Dimension and Lacunarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sushma">S. Sushma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Balasubramanian"> S. Balasubramanian</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20C.%20Latha"> K. C. Latha</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Sridhar"> R. Sridhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural texture measures are used to address the aspect of breast cancer risk assessment in screening mammograms. The current study investigates whether texture properties characterized by local Fractal Dimension (FD) and lacunarity contribute to assess breast cancer risk. Fractal Dimension represents the complexity while the lacunarity characterize the gap of a fractal dimension. In this paper, we present our result confirming that the lacunarity value resulted in algorithm using mammogram images states that level of lacunarity will be low when the Fractal Dimension value will be high. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20analysis" title=" image analysis"> image analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=lacunarity" title=" lacunarity"> lacunarity</a>, <a href="https://publications.waset.org/abstracts/search?q=mammogram" title=" mammogram"> mammogram</a> </p> <a href="https://publications.waset.org/abstracts/13593/lacunarity-measures-on-mammographic-image-applying-fractal-dimension-and-lacunarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3705</span> A Note on the Fractal Dimension of Mandelbrot Set and Julia Sets in Misiurewicz Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Boussoufi">O. Boussoufi</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Lamrini%20Uahabi"> K. Lamrini Uahabi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Atounti"> M. Atounti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this paper is to calculate the fractal dimension of some Julia Sets and Mandelbrot Set in the Misiurewicz Points. Using Matlab to generate the Julia Sets images that match the Misiurewicz points and using a Fractal software, we were able to find different measures that characterize those fractals in textures and other features. We are actually focusing on fractal dimension and the error calculated by the software. When executing the given equation of regression or the log-log slope of image a Box Counting method is applied to the entire image, and chosen settings are available in a FracLAc Program. Finally, a comparison is done for each image corresponding to the area (boundary) where Misiurewicz Point is located. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=box%20counting" title="box counting">box counting</a>, <a href="https://publications.waset.org/abstracts/search?q=FracLac" title=" FracLac"> FracLac</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=Julia%20Sets" title=" Julia Sets"> Julia Sets</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandelbrot%20Set" title=" Mandelbrot Set"> Mandelbrot Set</a>, <a href="https://publications.waset.org/abstracts/search?q=Misiurewicz%20Points" title=" Misiurewicz Points"> Misiurewicz Points</a> </p> <a href="https://publications.waset.org/abstracts/88210/a-note-on-the-fractal-dimension-of-mandelbrot-set-and-julia-sets-in-misiurewicz-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3704</span> A Novel Image Steganography Scheme Based on Mandelbrot Fractal </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20H.%20M.%20Al-Helali">Adnan H. M. Al-Helali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamza%20A.%20Ali"> Hamza A. Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Growth of censorship and pervasive monitoring on the Internet, Steganography arises as a new means of achieving secret communication. Steganography is the art and science of embedding information within electronic media used by common applications and systems. Generally, hiding information of multimedia within images will change some of their properties that may introduce few degradation or unusual characteristics. This paper presents a new image steganography approach for hiding information of multimedia (images, text, and audio) using generated Mandelbrot Fractal image as a cover. The proposed technique has been extensively tested with different images. The results show that the method is a very secure means of hiding and retrieving steganographic information. Experimental results demonstrate that an effective improvement in the values of the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Cross Correlation (NCC) and Image Fidelity (IF) over the previous techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20image" title="fractal image">fractal image</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20hiding" title=" information hiding"> information hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandelbrot%20et%20fractal" title=" Mandelbrot et fractal"> Mandelbrot et fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=steganography" title=" steganography"> steganography</a> </p> <a href="https://publications.waset.org/abstracts/21666/a-novel-image-steganography-scheme-based-on-mandelbrot-fractal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21666.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3703</span> A Novel Image Steganography Method Based on Mandelbrot Fractal </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20H.%20M.%20Al-Helali">Adnan H. M. Al-Helali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamza%20A.%20Ali"> Hamza A. Ali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The growth of censorship and pervasive monitoring on the Internet, Steganography arises as a new means of achieving secret communication. Steganography is the art and science of embedding information within electronic media used by common applications and systems. Generally, hiding information of multimedia within images will change some of their properties that may introduce few degradation or unusual characteristics. This paper presents a new image steganography approach for hiding information of multimedia (images, text, and audio) using generated Mandelbrot Fractal image as a cover. The proposed technique has been extensively tested with different images. The results show that the method is a very secure means of hiding and retrieving steganographic information. Experimental results demonstrate that an effective improvement in the values of the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Normalized Cross Correlation (NCC), and Image Fidelity (IF) over the pervious techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20image" title="fractal image">fractal image</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20hiding" title=" information hiding"> information hiding</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandelbrot%20set%20fractal" title=" Mandelbrot set fractal"> Mandelbrot set fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=steganography" title=" steganography"> steganography</a> </p> <a href="https://publications.waset.org/abstracts/21625/a-novel-image-steganography-method-based-on-mandelbrot-fractal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">618</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3702</span> Medical Image Compression Based on Region of Interest: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudeepti%20Dayal">Sudeepti Dayal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelesh%20Gupta"> Neelesh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In terms of transmission, bigger the size of any image, longer the time the channel takes for transmission. It is understood that the bandwidth of the channel is fixed. Therefore, if the size of an image is reduced, a larger number of data or images can be transmitted over the channel. Compression is the technique used to reduce the size of an image. In terms of storage, compression reduces the file size which it occupies on the disk. Any image is based on two parameters, region of interest and non-region of interest. There are several algorithms of compression that compress the data more economically. In this paper we have reviewed region of interest and non-region of interest based compression techniques and the algorithms which compress the image most efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20ratio" title="compression ratio">compression ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a> </p> <a href="https://publications.waset.org/abstracts/43380/medical-image-compression-based-on-region-of-interest-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">375</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3701</span> A High Compression Ratio for a Losseless Image Compression Based on the Arithmetic Coding with the Sorted Run Length Coding: Meteosat Second Generation Image Compression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cherifi%20Mehdi">Cherifi Mehdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image compression is the heart of several multimedia techniques. It is used to reduce the number of bits required to represent an image. Meteosat Second Generation (MSG) satellite allows the acquisition of 12 image files every 15 minutes and that results in a large databases sizes. In this paper, a novel image compression method based on the arithmetic coding with the sorted Run Length Coding (SRLC) for MSG images is proposed. The SRLC allows us to find the occurrence of the consecutive pixels of the original image to create a sorted run. The arithmetic coding allows the encoding of the sorted data of the previous stage to retrieve a unique code word that represents a binary code stream in the sorted order to boost the compression ratio. Through this article, we show that our method can perform the best results concerning compression ratio and bit rate unlike the method based on the Run Length Coding (RLC) and the arithmetic coding. Evaluation criteria like the compression ratio and the bit rate allow the confirmation of the efficiency of our method of image compression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=arithmetic%20coding" title=" arithmetic coding"> arithmetic coding</a>, <a href="https://publications.waset.org/abstracts/search?q=Run%20Length%20Coding" title=" Run Length Coding"> Run Length Coding</a>, <a href="https://publications.waset.org/abstracts/search?q=RLC" title=" RLC"> RLC</a>, <a href="https://publications.waset.org/abstracts/search?q=Sorted%20Run%20Length%20Coding" title=" Sorted Run Length Coding"> Sorted Run Length Coding</a>, <a href="https://publications.waset.org/abstracts/search?q=SRLC" title=" SRLC"> SRLC</a>, <a href="https://publications.waset.org/abstracts/search?q=Meteosat%20Second%20Generation" title=" Meteosat Second Generation"> Meteosat Second Generation</a>, <a href="https://publications.waset.org/abstracts/search?q=MSG" title=" MSG"> MSG</a> </p> <a href="https://publications.waset.org/abstracts/16704/a-high-compression-ratio-for-a-losseless-image-compression-based-on-the-arithmetic-coding-with-the-sorted-run-length-coding-meteosat-second-generation-image-compression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16704.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">354</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3700</span> Meteosat Second Generation Image Compression Based on the Radon Transform and Linear Predictive Coding: Comparison and Performance </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cherifi%20Mehdi">Cherifi Mehdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image compression is used to reduce the number of bits required to represent an image. The Meteosat Second Generation satellite (MSG) allows the acquisition of 12 image files every 15 minutes. Which results a large databases sizes. The transform selected in the images compression should contribute to reduce the data representing the images. The Radon transform retrieves the Radon points that represent the sum of the pixels in a given angle for each direction. Linear predictive coding (LPC) with filtering provides a good decorrelation of Radon points using a Predictor constitute by the Symmetric Nearest Neighbor filter (SNN) coefficients, which result losses during decompression. Finally, Run Length Coding (RLC) gives us a high and fixed compression ratio regardless of the input image. In this paper, a novel image compression method based on the Radon transform and linear predictive coding (LPC) for MSG images is proposed. MSG image compression based on the Radon transform and the LPC provides a good compromise between compression and quality of reconstruction. A comparison of our method with other whose two based on DCT and one on DWT bi-orthogonal filtering is evaluated to show the power of the Radon transform in its resistibility against the quantization noise and to evaluate the performance of our method. Evaluation criteria like PSNR and the compression ratio allows showing the efficiency of our method of compression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=radon%20transform" title=" radon transform"> radon transform</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20predictive%20coding%20%28LPC%29" title=" linear predictive coding (LPC)"> linear predictive coding (LPC)</a>, <a href="https://publications.waset.org/abstracts/search?q=run%20lengthcoding%20%28RLC%29" title=" run lengthcoding (RLC)"> run lengthcoding (RLC)</a>, <a href="https://publications.waset.org/abstracts/search?q=meteosat%20second%20generation%20%28MSG%29" title=" meteosat second generation (MSG)"> meteosat second generation (MSG)</a> </p> <a href="https://publications.waset.org/abstracts/16434/meteosat-second-generation-image-compression-based-on-the-radon-transform-and-linear-predictive-coding-comparison-and-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16434.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3699</span> Image Compression Using Block Power Method for SVD Decomposition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Asnaoui%20Khalid">El Asnaoui Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Chawki%20Youness"> Chawki Youness</a>, <a href="https://publications.waset.org/abstracts/search?q=Aksasse%20Brahim"> Aksasse Brahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ouanan%20Mohammed"> Ouanan Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In these recent decades, the important and fast growth in the development and demand of multimedia products is contributing to an insufficient in the bandwidth of device and network storage memory. Consequently, the theory of data compression becomes more significant for reducing the data redundancy in order to save more transfer and storage of data. In this context, this paper addresses the problem of the lossless and the near-lossless compression of images. This proposed method is based on Block SVD Power Method that overcomes the disadvantages of Matlab's SVD function. The experimental results show that the proposed algorithm has a better compression performance compared with the existing compression algorithms that use the Matlab's SVD function. In addition, the proposed approach is simple and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=SVD" title=" SVD"> SVD</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20SVD%20power%20method" title=" block SVD power method"> block SVD power method</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20compression" title=" lossless compression"> lossless compression</a>, <a href="https://publications.waset.org/abstracts/search?q=near%20lossless" title=" near lossless"> near lossless</a> </p> <a href="https://publications.waset.org/abstracts/34041/image-compression-using-block-power-method-for-svd-decomposition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34041.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3698</span> Image Compression on Region of Interest Based on SPIHT Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudeepti%20Dayal">Sudeepti Dayal</a>, <a href="https://publications.waset.org/abstracts/search?q=Neelesh%20Gupta"> Neelesh Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Compression%20ratio" title="Compression ratio">Compression ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=SPIHT" title=" SPIHT"> SPIHT</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT" title=" DCT"> DCT</a> </p> <a href="https://publications.waset.org/abstracts/43377/image-compression-on-region-of-interest-based-on-spiht-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3697</span> A Neural Approach for Color-Textured Images Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalid%20Salhi">Khalid Salhi</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Miloud%20Jaara"> El Miloud Jaara</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Talibi%20Alaoui"> Mohammed Talibi Alaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=segmentation" title="segmentation">segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=color-texture" title=" color-texture"> color-texture</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal" title=" fractal"> fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=watershed" title=" watershed"> watershed</a> </p> <a href="https://publications.waset.org/abstracts/51740/a-neural-approach-for-color-textured-images-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3696</span> Numerical Implementation and Testing of Fractioning Estimator Method for the Box-Counting Dimension of Fractal Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Ter%C3%A1n%20Salcedo">Abraham Terán Salcedo</a>, <a href="https://publications.waset.org/abstracts/search?q=Didier%20Samayoa%20Ochoa"> Didier Samayoa Ochoa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a numerical implementation of a method for estimating the box-counting dimension of self-avoiding curves on a planar space, fractal objects captured on digital images; this method is named fractioning estimator. Classical methods of digital image processing, such as noise filtering, contrast manipulation, and thresholding, among others, are used in order to obtain binary images that are suitable for performing the necessary computations of the fractioning estimator. A user interface is developed for performing the image processing operations and testing the fractioning estimator on different captured images of real-life fractal objects. To analyze the results, the estimations obtained through the fractioning estimator are compared to the results obtained through other methods that are already implemented on different available software for computing and estimating the box-counting dimension. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=box-counting" title="box-counting">box-counting</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20image%20processing" title=" digital image processing"> digital image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20method" title=" numerical method"> numerical method</a> </p> <a href="https://publications.waset.org/abstracts/160901/numerical-implementation-and-testing-of-fractioning-estimator-method-for-the-box-counting-dimension-of-fractal-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160901.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3695</span> A Survey on Lossless Compression of Bayer Color Filter Array Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alina%20Trifan">Alina Trifan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ant%C3%B3nio%20J.%20R.%20Neves"> António J. R. Neves</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although most digital cameras acquire images in a raw format, based on a Color Filter Array that arranges RGB color filters on a square grid of photosensors, most image compression techniques do not use the raw data; instead, they use the rgb result of an interpolation algorithm of the raw data. This approach is inefficient and by performing a lossless compression of the raw data, followed by pixel interpolation, digital cameras could be more power efficient and provide images with increased resolution given that the interpolation step could be shifted to an external processing unit. In this paper, we conduct a survey on the use of lossless compression algorithms with raw Bayer images. Moreover, in order to reduce the effect of the transition between colors that increase the entropy of the raw Bayer image, we split the image into three new images corresponding to each channel (red, green and blue) and we study the same compression algorithms applied to each one individually. This simple pre-processing stage allows an improvement of more than 15% in predictive based methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayer%20image" title="bayer image">bayer image</a>, <a href="https://publications.waset.org/abstracts/search?q=CFA" title=" CFA"> CFA</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20compression" title=" lossless compression"> lossless compression</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20coding%20standards" title=" image coding standards"> image coding standards</a> </p> <a href="https://publications.waset.org/abstracts/39918/a-survey-on-lossless-compression-of-bayer-color-filter-array-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39918.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">321</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3694</span> Single Feed Circularly Polarized Poly Fractal Antenna for Wireless Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20V.%20Reddy">V. V. Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20V.%20Sarma"> N. V. Sarma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A circularly polarized fractal boundary microstrip antenna is presented. The sides of a square patch along x-axis, y-axis are replaced with Minkowski and Koch curves correspondingly. By using the fractal curves as edges, asymmetry in the structure is created to excite two orthogonal modes for circular polarization (CP) operation. The indentation factors of the fractal curves are optimized for pure CP. The simulated results of the novel poly fractal antenna are demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal" title="fractal">fractal</a>, <a href="https://publications.waset.org/abstracts/search?q=circular%20polarization" title=" circular polarization"> circular polarization</a>, <a href="https://publications.waset.org/abstracts/search?q=Minkowski" title=" Minkowski"> Minkowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Koch" title=" Koch"> Koch</a> </p> <a href="https://publications.waset.org/abstracts/16535/single-feed-circularly-polarized-poly-fractal-antenna-for-wireless-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3693</span> PathoPy2.0: Application of Fractal Geometry for Early Detection and Histopathological Analysis of Lung Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rhea%20Kapoor">Rhea Kapoor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractals" title="fractals">fractals</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathological%20analysis" title=" histopathological analysis"> histopathological analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer" title=" lung cancer"> lung cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=Minkowski%20dimension" title=" Minkowski dimension"> Minkowski dimension</a> </p> <a href="https://publications.waset.org/abstracts/96476/pathopy20-application-of-fractal-geometry-for-early-detection-and-histopathological-analysis-of-lung-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96476.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3692</span> Image Compression Based on Regression SVM and Biorthogonal Wavelets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zikiou%20Nadia">Zikiou Nadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Lahdir%20Mourad"> Lahdir Mourad</a>, <a href="https://publications.waset.org/abstracts/search?q=Ameur%20Soltane"> Ameur Soltane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20discrete%20wavelet%20transform%20%28DWT-2D%29" title=" 2D discrete wavelet transform (DWT-2D)"> 2D discrete wavelet transform (DWT-2D)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title=" support vector regression (SVR)"> support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20Kernels" title=" SVM Kernels"> SVM Kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=run-length" title=" run-length"> run-length</a>, <a href="https://publications.waset.org/abstracts/search?q=arithmetic%20coding" title=" arithmetic coding"> arithmetic coding</a> </p> <a href="https://publications.waset.org/abstracts/17954/image-compression-based-on-regression-svm-and-biorthogonal-wavelets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17954.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3691</span> QCARNet: Networks for Quality-Adaptive Compression Artifact</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seung%20Ho%20Park">Seung Ho Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Young%20Su%20Moon"> Young Su Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Nam%20Ik%20Cho"> Nam Ik Cho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a convolution neural network (CNN) for quality adaptive compression artifact reduction named QCARNet. The proposed method is different from the existing discriminative models that learn a specific model at a certain quality level. The method is composed of a quality estimation CNN (QECNN) and a compression artifact reduction CNN (CARCNN), which are two functionally separate CNNs. By connecting the QECNN and CARCNN, each CARCNN layer is able to adaptively reduce compression artifacts and preserve details depending on the estimated quality level map generated by the QECNN. We experimentally demonstrate that the proposed method achieves better performance compared to other state-of-the-art blind compression artifact reduction methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20artifact%20reduction" title="compression artifact reduction">compression artifact reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=deblocking" title=" deblocking"> deblocking</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title=" image denoising"> image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20restoration" title=" image restoration"> image restoration</a> </p> <a href="https://publications.waset.org/abstracts/108816/qcarnet-networks-for-quality-adaptive-compression-artifact" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108816.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3690</span> An Online 3D Modeling Method Based on a Lossless Compression Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiankang%20Wang">Jiankang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyang%20Yu"> Hongyang Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a portable online 3D modeling method. The method first utilizes a depth camera to collect data and compresses the depth data using a frame-by-frame lossless data compression method. The color image is encoded using the H.264 encoding format. After the cloud obtains the color image and depth image, a 3D modeling method based on bundlefusion is used to complete the 3D modeling. The results of this study indicate that this method has the characteristics of portability, online, and high efficiency and has a wide range of application prospects. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title="3D reconstruction">3D reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=bundlefusion" title=" bundlefusion"> bundlefusion</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20compression" title=" lossless compression"> lossless compression</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20image" title=" depth image"> depth image</a> </p> <a href="https://publications.waset.org/abstracts/163266/an-online-3d-modeling-method-based-on-a-lossless-compression-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">82</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3689</span> Design of a Novel CPW Fed Fractal Antenna for UWB</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20El%20Hamdouni">A. El Hamdouni</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Zbitou"> J. Zbitou</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tajmouati"> A. Tajmouati</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20El%20Abdellaoui"> L. El Abdellaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Errkik"> A. Errkik</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tribak"> A. Tribak</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Latrach"> M. Latrach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a novel fractal antenna structure proposed for UWB (Ultra – Wideband) applications. The frequency band 3.1-10.6 GHz released by FCC (Federal Communication Commission) as the commercial operation of UWB has been chosen as frequency range for this antenna based on coplanar waveguide (CPW) feed and circular shapes fulfilled according to fractal geometry. The proposed antenna is validated and designed by using an FR4 substrate with overall area of 34 x 43 mm2. The simulated results performed by CST-Microwave Studio and compared by ADS (Advanced Design System) show good matching input impedance with return loss less than -10 dB between 2.9 GHz and 11 GHz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fractal%20antenna" title="Fractal antenna">Fractal antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=Fractal%20Geometry" title=" Fractal Geometry"> Fractal Geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=CPW%20Feed" title=" CPW Feed"> CPW Feed</a>, <a href="https://publications.waset.org/abstracts/search?q=UWB" title=" UWB"> UWB</a>, <a href="https://publications.waset.org/abstracts/search?q=FCC" title=" FCC"> FCC</a> </p> <a href="https://publications.waset.org/abstracts/17070/design-of-a-novel-cpw-fed-fractal-antenna-for-uwb" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17070.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">388</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3688</span> Trabecular Texture Analysis Using Fractal Metrics for Bone Fragility Assessment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Harrar">Khaled Harrar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Jennane"> Rachid Jennane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is the discrimination of 28 postmenopausal with osteoporotic femoral fractures from an age-matched control group of 28 women using texture analysis based on fractals. Two pre-processing approaches are applied on radiographic images; these techniques are compared to highlight the choice of the pre-processing method. Furthermore, the values of the fractal dimension are compared to those of the fractal signature in terms of the classification of the two populations. In a second analysis, the BMD measure at proximal femur was compared to the fractal analysis, the latter, which is a non-invasive technique, allowed a better discrimination; the results confirm that the fractal analysis of texture on calcaneus radiographs is able to discriminate osteoporotic patients with femoral fracture from controls. This discrimination was efficient compared to that obtained by BMD alone. It was also present in comparing subgroups with overlapping values of BMD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=osteoporosis" title="osteoporosis">osteoporosis</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20signature" title=" fractal signature"> fractal signature</a>, <a href="https://publications.waset.org/abstracts/search?q=bone%20mineral%20density" title=" bone mineral density"> bone mineral density</a> </p> <a href="https://publications.waset.org/abstracts/28859/trabecular-texture-analysis-using-fractal-metrics-for-bone-fragility-assessment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28859.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">425</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3687</span> Normalized Compression Distance Based Scene Alteration Analysis of a Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lakshay%20Kharbanda">Lakshay Kharbanda</a>, <a href="https://publications.waset.org/abstracts/search?q=Aabhas%20Chauhan"> Aabhas Chauhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an application of Normalized Compression Distance (NCD) to detect notable scene alterations occurring in videos is presented. Several research groups have been developing methods to perform image classification using NCD, a computable approximation to Normalized Information Distance (NID) by studying the degree of similarity in images. The timeframes where significant aberrations between the frames of a video have occurred have been identified by obtaining a threshold NCD value, using two compressors: LZMA and BZIP2 and defining scene alterations using Pixel Difference Percentage metrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolmogorov%20complexity" title=" Kolmogorov complexity"> Kolmogorov complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=normalized%20compression%20distance" title=" normalized compression distance"> normalized compression distance</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20mean%20square%20error" title=" root mean square error"> root mean square error</a> </p> <a href="https://publications.waset.org/abstracts/54601/normalized-compression-distance-based-scene-alteration-analysis-of-a-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54601.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3686</span> Objective Evaluation on Medical Image Compression Using Wavelet Transformation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amhimmid%20Mohammed%20Saffour">Amhimmid Mohammed Saffour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Mohamed%20Abdullah"> Mustafa Mohamed Abdullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20image" title="medical image">medical image</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab" title=" Matlab"> Matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title=" image compression"> image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%27s" title=" wavelet's"> wavelet's</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20evaluation" title=" objective evaluation"> objective evaluation</a> </p> <a href="https://publications.waset.org/abstracts/45414/objective-evaluation-on-medical-image-compression-using-wavelet-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">285</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3685</span> Use of Fractal Geometry in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20geometry" title="fractal geometry">fractal geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classifier" title=" classifier"> classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a> </p> <a href="https://publications.waset.org/abstracts/141274/use-of-fractal-geometry-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141274.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3684</span> Introduction of Artificial Intelligence for Estimating Fractal Dimension and Its Applications in the Medical Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zerroug%20Abdelhamid">Zerroug Abdelhamid</a>, <a href="https://publications.waset.org/abstracts/search?q=Danielle%20Chassoux"> Danielle Chassoux</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=simulation" title="simulation">simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=cancerous%20tumor" title=" cancerous tumor"> cancerous tumor</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20fields" title=" Markov fields"> Markov fields</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=recovering" title=" recovering "> recovering </a> </p> <a href="https://publications.waset.org/abstracts/18665/introduction-of-artificial-intelligence-for-estimating-fractal-dimension-and-its-applications-in-the-medical-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3683</span> Embedded Digital Image System </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dawei%20Li">Dawei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Liu"> Cheng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yiteng%20Liu"> Yiteng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an embedded digital image system for Chinese space environment vertical exploration sounding rocket. In order to record the flight status of the sounding rocket as well as the payloads, an onboard embedded image processing system based on ADV212, a JPEG2000 compression chip, is designed in this paper. Since the sounding rocket is not designed to be recovered, all image data should be transmitted to the ground station before the re-entry while the downlink band used for the image transmission is only about 600 kbps. Under the same condition of compression ratio compared with other algorithm, JPEG2000 standard algorithm can achieve better image quality. So JPEG2000 image compression is applied under this condition with a limited downlink data band. This embedded image system supports lossless to 200:1 real time compression, with two cameras to monitor nose ejection and motor separation, and two cameras to monitor boom deployment. The encoder, ADV7182, receives PAL signal from the camera, then output the ITU-R BT.656 signal to ADV212. ADV7182 switches between four input video channels as the program sequence. Two SRAMs are used for Ping-pong operation and one 512 Mb SDRAM for buffering high frame-rate images. The whole image system has the characteristics of low power dissipation, low cost, small size and high reliability, which is rather suitable for this sounding rocket application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ADV212" title="ADV212">ADV212</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20system" title=" image system"> image system</a>, <a href="https://publications.waset.org/abstracts/search?q=JPEG2000" title=" JPEG2000"> JPEG2000</a>, <a href="https://publications.waset.org/abstracts/search?q=sounding%20rocket" title=" sounding rocket"> sounding rocket</a> </p> <a href="https://publications.waset.org/abstracts/37615/embedded-digital-image-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3682</span> Bitplanes Gray-Level Image Encryption Approach Using Arnold Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Abdrhman%20M.%20Ukasha">Ali Abdrhman M. Ukasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSPCE%20method" title="SSPCE method">SSPCE method</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression-salt-%20peppers%20attacks" title=" image compression-salt- peppers attacks"> image compression-salt- peppers attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=bitplanes%20decomposition" title=" bitplanes decomposition"> bitplanes decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20image%20encryption" title=" lossless image encryption"> lossless image encryption</a> </p> <a href="https://publications.waset.org/abstracts/14573/bitplanes-gray-level-image-encryption-approach-using-arnold-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14573.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3681</span> New Efficient Method for Coding Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Walaa%20M.Abd-Elhafiez">Walaa M.Abd-Elhafiez</a>, <a href="https://publications.waset.org/abstracts/search?q=Wajeb%20Gharibi"> Wajeb Gharibi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20image" title=" color image"> color image</a>, <a href="https://publications.waset.org/abstracts/search?q=q-coder" title=" q-coder"> q-coder</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=edge-detection" title=" edge-detection"> edge-detection</a> </p> <a href="https://publications.waset.org/abstracts/2342/new-efficient-method-for-coding-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2342.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">330</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3680</span> Bitplanes Image Encryption/Decryption Using Edge Map (SSPCE Method) and Arnold Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20A.%20Ukasha">Ali A. Ukasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data security needed in data transmission, storage, and communication to ensure the security. The single step parallel contour extraction (SSPCE) method is used to create the edge map as a key image from the different Gray level/Binary image. Performing the X-OR operation between the key image and each bit plane of the original image for image pixel values change purpose. The Arnold transform used to changes the locations of image pixels as image scrambling process. Experiments have demonstrated that proposed algorithm can fully encrypt 2D Gary level image and completely reconstructed without any distortion. Also shown that the analyzed algorithm have extremely large security against some attacks like salt & pepper and JPEG compression. Its proof that the Gray level image can be protected with a higher security level. The presented method has easy hardware implementation and suitable for multimedia protection in real time applications such as wireless networks and mobile phone services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSPCE%20method" title="SSPCE method">SSPCE method</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title=" image compression"> image compression</a>, <a href="https://publications.waset.org/abstracts/search?q=salt%20and%0D%0Apeppers%20attacks" title=" salt and peppers attacks"> salt and peppers attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=bitplanes%20decomposition" title=" bitplanes decomposition"> bitplanes decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnold%20transform" title=" Arnold transform"> Arnold transform</a>, <a href="https://publications.waset.org/abstracts/search?q=lossless%20image%20encryption" title=" lossless image encryption"> lossless image encryption</a> </p> <a href="https://publications.waset.org/abstracts/14570/bitplanes-image-encryptiondecryption-using-edge-map-sspce-method-and-arnold-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14570.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3679</span> FRATSAN: A New Software for Fractal Analysis of Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Namazi">Hamidreza Namazi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fractal analysis is assessing fractal characteristics of data. It consists of several methods to assign fractal characteristics to a dataset which may be a theoretical dataset or a pattern or signal extracted from phenomena including natural geometric objects, sound, market fluctuations, heart rates, digital images, molecular motion, networks, etc. Fractal analysis is now widely used in all areas of science. An important limitation of fractal analysis is that arriving at an empirically determined fractal dimension does not necessarily prove that a pattern is fractal; rather, other essential characteristics have to be considered. For this purpose a Visual C++ based software called FRATSAN (FRActal Time Series ANalyser) was developed which extract information from signals through three measures. These measures are Fractal Dimensions, Jeﬀrey’s Measure and Hurst Exponent. After computing these measures, the software plots the graphs for each measure. Besides computing three measures the software can classify whether the signal is fractal or no. In fact, the software uses a dynamic method of analysis for all the measures. A sliding window is selected with a value equal to 10% of the total number of data entries. This sliding window is moved one data entry at a time to obtain all the measures. This makes the computation very sensitive to slight changes in data, thereby giving the user an acute analysis of the data. In order to test the performance of this software a set of EEG signals was given as input and the results were computed and plotted. This software is useful not only for fundamental fractal analysis of signals but can be used for other purposes. For instance by analyzing the Hurst exponent plot of a given EEG signal in patients with epilepsy the onset of seizure can be predicted by noticing the sudden changes in the plot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals" title="EEG signals">EEG signals</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20analysis" title=" fractal analysis"> fractal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimension" title=" fractal dimension"> fractal dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=hurst%20exponent" title=" hurst exponent"> hurst exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=Je%EF%AC%80rey%E2%80%99s%20measure" title=" Jeﬀrey’s measure"> Jeﬀrey’s measure</a> </p> <a href="https://publications.waset.org/abstracts/18806/fratsan-a-new-software-for-fractal-analysis-of-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18806.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">467</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=fractal%20image%20compression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=fractal%20image%20compression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=fractal%20image%20compression&page=4">4</a></li> <li class="page-item"><a class="page-link" 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