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compression</title> <meta name="description" content="Search results for: video compression"> <meta name="keywords" content="video 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 class="d-block d-lg-none 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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="video 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> 1866</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: video compression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1866</span> Video Compression Using Contourlet Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Delara%20Kazempour">Delara Kazempour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mashallah%20Abasi%20Dezfuli"> Mashallah Abasi Dezfuli</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Javidan"> Reza Javidan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video compression used for channels with limited bandwidth and storage devices has limited storage capabilities. One of the most popular approaches in video compression is the usage of different transforms. Discrete cosine transform is one of the video compression methods that have some problems such as blocking, noising and high distortion inappropriate effect in compression ratio. wavelet transform is another approach is better than cosine transforms in balancing of compression and quality but the recognizing of curve curvature is so limit. Because of the importance of the compression and problems of the cosine and wavelet transforms, the contourlet transform is most popular in video compression. In the new proposed method, we used contourlet transform in video image compression. Contourlet transform can save details of the image better than the previous transforms because this transform is multi-scale and oriented. This transform can recognize discontinuity such as edges. In this approach we lost data less than previous approaches. Contourlet transform finds discrete space structure. This transform is useful for represented of two dimension smooth images. This transform, produces compressed images with high compression ratio along with texture and edge preservation. Finally, the results show that the majority of the images, the parameters of the mean square error and maximum signal-to-noise ratio of the new method based contourlet transform compared to wavelet transform are improved but in most of the images, the parameters of the mean square error and maximum signal-to-noise ratio in the cosine transform is better than the method based on contourlet transform. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title="video compression">video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=contourlet%20transform" title=" contourlet transform"> contourlet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20cosine%20transform" title=" discrete cosine transform"> discrete cosine transform</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform" title=" wavelet transform"> wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/6930/video-compression-using-contourlet-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6930.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">444</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">1865</span> H.263 Based Video Transceiver for Wireless Camera System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Won-Ho%20Kim">Won-Ho Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20video%20transceiver" title="wireless video transceiver">wireless video transceiver</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance%20camera" title=" video surveillance camera"> video surveillance camera</a>, <a href="https://publications.waset.org/abstracts/search?q=H.263%20video%20encoding%20digital%20signal%20processing" title=" H.263 video encoding digital signal processing"> H.263 video encoding digital signal processing</a> </p> <a href="https://publications.waset.org/abstracts/12951/h263-based-video-transceiver-for-wireless-camera-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12951.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">364</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">1864</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">1863</span> Efficient Video Compression Technique Using Convolutional Neural Networks and Generative Adversarial Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Karthick">P. Karthick</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Mahesh"> K. Mahesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title="video compression">video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering" title=" K-means clustering"> K-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20network" title=" generative adversarial network"> generative adversarial network</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel%20visualization" title=" pixel visualization"> pixel visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20gradient%20descent" title=" stochastic gradient descent"> stochastic gradient descent</a>, <a href="https://publications.waset.org/abstracts/search?q=frame%20per%20second%20extraction" title=" frame per second extraction"> frame per second extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20channel%20extraction" title=" RGB channel extraction"> RGB channel extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=self-detection%20and%20deciding%20system" title=" self-detection and deciding system"> self-detection and deciding system</a> </p> <a href="https://publications.waset.org/abstracts/138827/efficient-video-compression-technique-using-convolutional-neural-networks-and-generative-adversarial-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138827.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">187</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">1862</span> Efficient Storage and Intelligent Retrieval of Multimedia Streams Using H. 265</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Deepadharani"> C. Deepadharani</a>, <a href="https://publications.waset.org/abstracts/search?q=Garimella%20Archana"> Garimella Archana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dakshayani"> S. Dakshayani</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Logeshwaran"> D. Logeshwaran</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Jayakumar"> D. Jayakumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayarangan%20Natarajan"> Vijayarangan Natarajan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need of the hour for the customers who use a dial-up or a low broadband connection for their internet services is to access HD video data. This can be achieved by developing a new video format using H. 265. This is the latest video codec standard developed by ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) on April 2013. This new standard for video compression has the potential to deliver higher performance than the earlier standards such as H. 264/AVC. In comparison with H. 264, HEVC offers a clearer, higher quality image at half the original bitrate. At this lower bitrate, it is possible to transmit high definition videos using low bandwidth. It doubles the data compression ratio supporting 8K Ultra HD and resolutions up to 8192×4320. In the proposed model, we design a new video format which supports this H. 265 standard. The major areas of applications in the coming future would lead to enhancements in the performance level of digital television like Tata Sky and Sun Direct, BluRay Discs, Mobile Video, Video Conferencing and Internet and Live Video streaming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=access%20HD%20video" title="access HD video">access HD video</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20265%20video%20standard" title=" H. 265 video standard"> H. 265 video standard</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20performance" title=" high performance"> high performance</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20quality%20image" title=" high quality image"> high quality image</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20bandwidth" title=" low bandwidth"> low bandwidth</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20video%20format" title=" new video format"> new video format</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20streaming%20applications" title=" video streaming applications"> video streaming applications</a> </p> <a href="https://publications.waset.org/abstracts/1881/efficient-storage-and-intelligent-retrieval-of-multimedia-streams-using-h-265" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1881.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">1861</span> Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Azimi">Maryam Azimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Banitalebi-Dehkordi"> Amin Banitalebi-Dehkordi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuanyuan%20Dong"> Yuanyuan Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20T.%20Pourazad"> Mahsa T. Pourazad</a>, <a href="https://publications.waset.org/abstracts/search?q=Panos%20Nasiopoulos"> Panos Nasiopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While there exists a wide variety of Low Dynamic Range (LDR) quality metrics, only a limited number of metrics are designed specifically for the High Dynamic Range (HDR) content. With the introduction of HDR video compression standardization effort by international standardization bodies, the need for an efficient video quality metric for HDR applications has become more pronounced. The objective of this study is to compare the performance of the existing full-reference LDR and HDR video quality metrics on HDR content and identify the most effective one for HDR applications. To this end, a new HDR video data set is created, which consists of representative indoor and outdoor video sequences with different brightness, motion levels and different representing types of distortions. The quality of each distorted video in this data set is evaluated both subjectively and objectively. The correlation between the subjective and objective results confirm that VIF quality metric outperforms all to their tested metrics in the presence of the tested types of distortions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HDR" title="HDR">HDR</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20range" title=" dynamic range"> dynamic range</a>, <a href="https://publications.waset.org/abstracts/search?q=LDR" title=" LDR"> LDR</a>, <a href="https://publications.waset.org/abstracts/search?q=subjective%20evaluation" title=" subjective evaluation"> subjective evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title=" video compression"> video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=HEVC" title=" HEVC"> HEVC</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20quality%20metrics" title=" video quality metrics"> video quality metrics</a> </p> <a href="https://publications.waset.org/abstracts/18171/evaluating-the-performance-of-existing-full-reference-quality-metrics-on-high-dynamic-range-hdr-video-content" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18171.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">525</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">1860</span> Performance of High Efficiency Video Codec over Wireless Channels</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Ayyub%20Khan">Mohd Ayyub Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadeem%20Akhtar"> Nadeem Akhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to recent advances in wireless communication technologies and hand-held devices, there is a huge demand for video-based applications such as video surveillance, video conferencing, remote surgery, Digital Video Broadcast (DVB), IPTV, online learning courses, YouTube, WhatsApp, Instagram, Facebook, Interactive Video Games. However, the raw videos posses very high bandwidth which makes the compression a must before its transmission over the wireless channels. The High Efficiency Video Codec (HEVC) (also called H.265) is latest state-of-the-art video coding standard developed by the Joint effort of ITU-T and ISO/IEC teams. HEVC is targeted for high resolution videos such as 4K or 8K resolutions that can fulfil the recent demands for video services. The compression ratio achieved by the HEVC is twice as compared to its predecessor H.264/AVC for same quality level. The compression efficiency is generally increased by removing more correlation between the frames/pixels using complex techniques such as extensive intra and inter prediction techniques. As more correlation is removed, the chances of interdependency among coded bits increases. Thus, bit errors may have large effect on the reconstructed video. Sometimes even single bit error can lead to catastrophic failure of the reconstructed video. In this paper, we study the performance of HEVC bitstream over additive white Gaussian noise (AWGN) channel. Moreover, HEVC over Quadrature Amplitude Modulation (QAM) combined with forward error correction (FEC) schemes are also explored over the noisy channel. The video will be encoded using HEVC, and the coded bitstream is channel coded to provide some redundancies. The channel coded bitstream is then modulated using QAM and transmitted over AWGN channel. At the receiver, the symbols are demodulated and channel decoded to obtain the video bitstream. The bitstream is then used to reconstruct the video using HEVC decoder. It is observed that as the signal to noise ratio of channel is decreased the quality of the reconstructed video decreases drastically. Using proper FEC codes, the quality of the video can be restored up to certain extent. Thus, the performance analysis of HEVC presented in this paper may assist in designing the optimized code rate of FEC such that the quality of the reconstructed video is maximized over wireless channels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AWGN" title="AWGN">AWGN</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20error%20correction" title=" forward error correction"> forward error correction</a>, <a href="https://publications.waset.org/abstracts/search?q=HEVC" title=" HEVC"> HEVC</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20coding" title=" video coding"> video coding</a>, <a href="https://publications.waset.org/abstracts/search?q=QAM" title=" QAM"> QAM</a> </p> <a href="https://publications.waset.org/abstracts/92062/performance-of-high-efficiency-video-codec-over-wireless-channels" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92062.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">149</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">1859</span> Efficient DCT Architectures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mr.%20P.%20Suryaprasad">Mr. P. Suryaprasad</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Lalitha"> R. Lalitha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an efficient area and delay architectures for the implementation of one dimensional and two dimensional discrete cosine transform (DCT). These are supported to different lengths (4, 8, 16, and 32). DCT blocks are used in the different video coding standards for the image compression. The 2D- DCT calculation is made using the 2D-DCT separability property, such that the whole architecture is divided into two 1D-DCT calculations by using a transpose buffer. Based on the existing 1D-DCT architecture two different types of 2D-DCT architectures, folded and parallel types are implemented. Both of these two structures use the same transpose buffer. Proposed transpose buffer occupies less area and high speed than existing transpose buffer. Hence the area, low power and delay of both the 2D-DCT architectures are reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transposition%20buffer" title="transposition buffer">transposition buffer</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title=" video compression"> video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20cosine%20transform" title=" discrete cosine transform"> discrete cosine transform</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20efficiency%20video%20coding" title=" high efficiency video coding"> high efficiency video coding</a>, <a href="https://publications.waset.org/abstracts/search?q=two%20dimensional%20picture" title=" two dimensional picture"> two dimensional picture</a> </p> <a href="https://publications.waset.org/abstracts/33624/efficient-dct-architectures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33624.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">522</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">1858</span> H.264 Video Privacy Protection Method Using Regions of Interest Encryption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taekyun%20Doo">Taekyun Doo</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheongmin%20Ji"> Cheongmin Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Manpyo%20Hong"> Manpyo Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Like a closed-circuit television (CCTV), video surveillance system is widely placed for gathering video from unspecified people to prevent crime, surveillance, or many other purposes. However, abuse of CCTV brings about concerns of personal privacy invasions. In this paper, we propose an encryption method to protect personal privacy system in H.264 compressed video bitstream with encrypting only regions of interest (ROI). There is no need to change the existing video surveillance system. In addition, encrypting ROI in compressed video bitstream is a challenging work due to spatial and temporal drift errors. For this reason, we propose a novel drift mitigation method when ROI is encrypted. The proposed method was implemented by using JM reference software based on the H.264 compressed videos, and experimental results show the verification of our proposed methods and its effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.264%2FAVC" title="H.264/AVC">H.264/AVC</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20encryption" title=" video encryption"> video encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=post%20compression" title=" post compression"> post compression</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a> </p> <a href="https://publications.waset.org/abstracts/57651/h264-video-privacy-protection-method-using-regions-of-interest-encryption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57651.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">1857</span> Efficient Motion Estimation by Fast Three Step Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Kulkarni">S. M. Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20S.%20Bormane"> D. S. Bormane</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20L.%20Nalbalwar"> S. L. Nalbalwar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid development in the technology have dramatic impact on the medical health care field. Medical data base obtained with latest machines like CT Machine, MRI scanner requires large amount of memory storage and also it requires large bandwidth for transmission of data in telemedicine applications. Thus, there is need for video compression. As the database of medical images contain number of frames (slices), hence while coding of these images there is need of motion estimation. Motion estimation finds out movement of objects in an image sequence and gets motion vectors which represents estimated motion of object in the frame. In order to reduce temporal redundancy between successive frames of video sequence, motion compensation is preformed. In this paper three step search (TSS) block matching algorithm is implemented on different types of video sequences. It is shown that three step search algorithm produces better quality performance and less computational time compared with exhaustive full search algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=block%20matching" title="block matching">block matching</a>, <a href="https://publications.waset.org/abstracts/search?q=exhaustive%20search%20motion%20estimation" title=" exhaustive search motion estimation"> exhaustive search motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20step%20search" title=" three step search"> three step search</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title=" video compression"> video compression</a> </p> <a href="https://publications.waset.org/abstracts/23746/efficient-motion-estimation-by-fast-three-step-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23746.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">491</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">1856</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.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">601</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">1855</span> Video Summarization: Techniques and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaynab%20El%20Khattabi">Zaynab El Khattabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Youness%20Tabii"> Youness Tabii</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Benkaddour"> Abdelhamid Benkaddour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20summarization" title=" static summarization"> static summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20skimming" title=" video skimming"> video skimming</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20features" title=" semantic features"> semantic features</a> </p> <a href="https://publications.waset.org/abstracts/27644/video-summarization-techniques-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27644.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">401</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">1854</span> Evaluation of Video Quality Metrics and Performance Comparison on Contents Taken from Most Commonly Used Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pratik%20Dhabal%20Deo">Pratik Dhabal Deo</a>, <a href="https://publications.waset.org/abstracts/search?q=Manoj%20P."> Manoj P.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing number of social media users, the amount of video content available has also significantly increased. Currently, the number of smartphone users is at its peak, and many are increasingly using their smartphones as their main photography and recording devices. There have been a lot of developments in the field of Video Quality Assessment (VQA) and metrics like VMAF, SSIM etc. are said to be some of the best performing metrics, but the evaluation of these metrics is dominantly done on professionally taken video contents using professional tools, lighting conditions etc. No study particularly pinpointing the performance of the metrics on the contents taken by users on very commonly available devices has been done. Datasets that contain a huge number of videos from different high-end devices make it difficult to analyze the performance of the metrics on the content from most used devices even if they contain contents taken in poor lighting conditions using lower-end devices. These devices face a lot of distortions due to various factors since the spectrum of contents recorded on these devices is huge. In this paper, we have presented an analysis of the objective VQA metrics on contents taken only from most used devices and their performance on them, focusing on full-reference metrics. To carry out this research, we created a custom dataset containing a total of 90 videos that have been taken from three most commonly used devices, and android smartphone, an IOS smartphone and a DSLR. On the videos taken on each of these devices, the six most common types of distortions that users face have been applied on addition to already existing H.264 compression based on four reference videos. These six applied distortions have three levels of degradation each. A total of the five most popular VQA metrics have been evaluated on this dataset and the highest values and the lowest values of each of the metrics on the distortions have been recorded. Finally, it is found that blur is the artifact on which most of the metrics didn’t perform well. Thus, in order to understand the results better the amount of blur in the data set has been calculated and an additional evaluation of the metrics was done using HEVC codec, which is the next version of H.264 compression, on the camera that proved to be the sharpest among the devices. The results have shown that as the resolution increases, the performance of the metrics tends to become more accurate and the best performing metric among them is VQM with very few inconsistencies and inaccurate results when the compression applied is H.264, but when the compression is applied is HEVC, SSIM and VMAF have performed significantly better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distortion" title="distortion">distortion</a>, <a href="https://publications.waset.org/abstracts/search?q=metrics" title=" metrics"> metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=resolution" title=" resolution"> resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20quality%20assessment" title=" video quality assessment"> video quality assessment</a> </p> <a href="https://publications.waset.org/abstracts/145939/evaluation-of-video-quality-metrics-and-performance-comparison-on-contents-taken-from-most-commonly-used-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145939.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">203</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">1853</span> Lecture Video Indexing and Retrieval Using Topic Keywords</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Sandesh">B. J. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabha%20Jirgi"> Saurabha Jirgi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Vidya"> S. Vidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Prakash%20Eljer"> Prakash Eljer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowri%20Srinivasa"> Gowri Srinivasa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20indexing%20and%20retrieval" title="video indexing and retrieval">video indexing and retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=lecture%20videos" title=" lecture videos"> lecture videos</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20video%20search" title=" content based video search"> content based video search</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20indexing" title=" multimodal indexing"> multimodal indexing</a> </p> <a href="https://publications.waset.org/abstracts/77066/lecture-video-indexing-and-retrieval-using-topic-keywords" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77066.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">250</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">1852</span> Distributed Processing for Content Based Lecture Video Retrieval on Hadoop Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20S.%20N.%20Raju">U. S. N. Raju</a>, <a href="https://publications.waset.org/abstracts/search?q=Kothuri%20Sai%20Kiran"> Kothuri Sai Kiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Meena%20G.%20Kamal"> Meena G. Kamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinay%20Nikhil%20Pabba"> Vinay Nikhil Pabba</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20Kanaparthi"> Suresh Kanaparthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efﬁcient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20lectures" title="video lectures">video lectures</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data" title=" big video data"> big video data</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20retrieval" title=" video retrieval"> video retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=hadoop" title=" hadoop"> hadoop</a> </p> <a href="https://publications.waset.org/abstracts/26648/distributed-processing-for-content-based-lecture-video-retrieval-on-hadoop-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26648.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">534</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">1851</span> Video Stabilization Using Feature Point Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shamsundar%20Kulkarni">Shamsundar Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20stabilization" title="video stabilization">video stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20feature%20matching" title=" point feature matching"> point feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=salient%20points" title=" salient points"> salient points</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality%20measurement" title=" image quality measurement"> image quality measurement</a> </p> <a href="https://publications.waset.org/abstracts/57341/video-stabilization-using-feature-point-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57341.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">313</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">1850</span> Structural Analysis on the Composition of Video Game Virtual Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qin%20Luofeng">Qin Luofeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Siqi"> Shen Siqi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the 58 years since the first video game came into being, the video game industry is getting through an explosive evolution from then on. Video games exert great influence on society and become a reflection of public life to some extent. Video game virtual spaces are where activities are taking place like real spaces. And that’s the reason why some architects pay attention to video games. However, compared to the researches on the appearance of games, we observe a lack of theoretical comprehensive on the construction of video game virtual spaces. The research method of this paper is to collect literature and conduct theoretical research about the virtual space in video games firstly. And then analogizing the opinions on the space phenomena from the theory of literature and films. Finally, this paper proposes a three-layer framework for the construction of video game virtual spaces: “algorithmic space-narrative space players space”, which correspond to the exterior, expressive, affective parts of the game space. Also, we illustrate each sub-space according to numerous instances of published video games. Hoping this writing could promote the interactive development of video games and architecture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20game" title="video game">video game</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20space" title=" virtual space"> virtual space</a>, <a href="https://publications.waset.org/abstracts/search?q=narrativity" title=" narrativity"> narrativity</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20space" title=" social space"> social space</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20connection" title=" emotional connection"> emotional connection</a> </p> <a href="https://publications.waset.org/abstracts/118519/structural-analysis-on-the-composition-of-video-game-virtual-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118519.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">267</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">1849</span> Key Frame Based Video Summarization via Dependency Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janya%20Sainui">Janya Sainui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20measure" title=" dependency measure"> dependency measure</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20mutual%20information" title=" quadratic mutual information"> quadratic mutual information</a> </p> <a href="https://publications.waset.org/abstracts/75218/key-frame-based-video-summarization-via-dependency-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75218.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">266</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">1848</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">1847</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">1846</span> Hardware Implementation of Local Binary Pattern Based Two-Bit Transform Motion Estimation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seda%20Yavuz">Seda Yavuz</a>, <a href="https://publications.waset.org/abstracts/search?q=An%C4%B1l%20%C3%87elebi"> Anıl Çelebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aysun%20Ta%C5%9Fyap%C4%B1%20%C3%87elebi"> Aysun Taşyapı Çelebi</a>, <a href="https://publications.waset.org/abstracts/search?q=O%C4%9Fuzhan%20Urhan"> Oğuzhan Urhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, demand for using real-time video transmission capable devices is ever-increasing. So, high resolution videos have made efficient video compression techniques an essential component for capturing and transmitting video data. Motion estimation has a critical role in encoding raw video. Hence, various motion estimation methods are introduced to efficiently compress the video. Low bit‑depth representation based motion estimation methods facilitate computation of matching criteria and thus, provide small hardware footprint. In this paper, a hardware implementation of a two-bit transformation based low-complexity motion estimation method using local binary pattern approach is proposed. Image frames are represented in two-bit depth instead of full-depth by making use of the local binary pattern as a binarization approach and the binarization part of the hardware architecture is explained in detail. Experimental results demonstrate the difference between the proposed hardware architecture and the architectures of well-known low-complexity motion estimation methods in terms of important aspects such as resource utilization, energy and power consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binarization" title="binarization">binarization</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%20architecture" title=" hardware architecture"> hardware architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title=" motion estimation"> motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=two-bit%20transform" title=" two-bit transform"> two-bit transform</a> </p> <a href="https://publications.waset.org/abstracts/77730/hardware-implementation-of-local-binary-pattern-based-two-bit-transform-motion-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77730.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">311</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">1845</span> Comparison of Compression Properties of Stretchable Knitted Fabrics and Bi-Stretch Woven Fabrics for Compression Garments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Maqsood">Muhammad Maqsood</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasir%20Nawab"> Yasir Nawab</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Talha%20Ali%20Hamdani"> Syed Talha Ali Hamdani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stretchable fabrics have diverse applications ranging from casual apparel to performance sportswear and compression therapy. Compression therapy is the universally accepted treatment for the management of hypertrophic scarring after severe burns. Mostly stretchable knitted fabrics are used in compression therapy but in the recent past, some studies have also been found on bi-stretch woven fabrics being used as compression garments as they also have been found quite effective in the treatment of oedema. Therefore, the objective of the present study is to compare the compression properties of stretchable knitted and bi-stretch woven fabrics for compression garments. For this purpose four woven structures and four knitted structures were produced having the same areal density and their compression, comfort and mechanical properties were compared before and after 5, 10 and 15 washes. Four knitted structures used were single jersey, single locaste, plain pique and the honeycomb, whereas four woven structures produced were 1/1 plain, 2/1 twill, 3/1 twill and 4/1 twill. The compression properties of the produced samples were tested by using kikuhime pressure sensor and it was found that bi-stretch woven fabrics possessed better compression properties before and after washes and retain their durability after repeated use, whereas knitted stretchable fabrics lost their compression ability after repeated use and the required sub garment pressure of the knitted structures after 15 washes was almost half to that of woven bi-stretch fabrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20garments" title="compression garments">compression garments</a>, <a href="https://publications.waset.org/abstracts/search?q=knitted%20structures" title=" knitted structures"> knitted structures</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20textiles" title=" medical textiles"> medical textiles</a>, <a href="https://publications.waset.org/abstracts/search?q=woven%20bi-stretch" title=" woven bi-stretch"> woven bi-stretch</a> </p> <a href="https://publications.waset.org/abstracts/39769/comparison-of-compression-properties-of-stretchable-knitted-fabrics-and-bi-stretch-woven-fabrics-for-compression-garments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39769.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">412</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">1844</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">1843</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">1842</span> Video Shot Detection and Key Frame Extraction Using Faber-Shauder DWT and SVD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Assma%20Azeroual">Assma Azeroual</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Afdel"> Karim Afdel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Hajji"> Mohamed El Hajji</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Douzi"> Hassan Douzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FSDWT" title="FSDWT">FSDWT</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=shot%20detection" title=" shot detection"> shot detection</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a> </p> <a href="https://publications.waset.org/abstracts/18296/video-shot-detection-and-key-frame-extraction-using-faber-shauder-dwt-and-svd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18296.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">398</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">1841</span> A Novel Search Pattern for Motion Estimation in High Efficiency Video Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phong%20Nguyen">Phong Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Phap%20Nguyen"> Phap Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Thang%20Nguyen"> Thang Nguyen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High Efficiency Video Coding (HEVC) or H.265 Standard fulfills the demand of high resolution video storage and transmission since it achieves high compression ratio. However, it requires a huge amount of calculation. Since Motion Estimation (ME) block composes about 80 % of calculation load of HEVC, there are a lot of researches to reduce the computation cost. In this paper, we propose a new algorithm to lower the number of Motion Estimation’s searching points. The number of computing points in search pattern is down from 77 for Diamond Pattern and 81 for Square Pattern to only 31. Meanwhile, the Peak Signal to Noise Ratio (PSNR) and bit rate are almost equal to those of conventional patterns. The motion estimation time of new algorithm reduces by at 68.23%, 65.83%compared to the recommended search pattern of diamond pattern, square pattern, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title="motion estimation">motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=wide%20diamond" title=" wide diamond"> wide diamond</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20pattern" title=" search pattern"> search pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=H.265" title=" H.265"> H.265</a>, <a href="https://publications.waset.org/abstracts/search?q=test%20zone%20search" title=" test zone search"> test zone search</a>, <a href="https://publications.waset.org/abstracts/search?q=HM%20software" title=" HM software"> HM software</a> </p> <a href="https://publications.waset.org/abstracts/22368/a-novel-search-pattern-for-motion-estimation-in-high-efficiency-video-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22368.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">611</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">1840</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">1839</span> Compression Index Estimation by Water Content and Liquid Limit and Void Ratio Using Statistics Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lizhou%20Chen">Lizhou Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Belgaid"> Abdelhamid Belgaid</a>, <a href="https://publications.waset.org/abstracts/search?q=Assem%20Elsayed"> Assem Elsayed</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoming%20Yang"> Xiaoming Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compression index is essential in foundation settlement calculation. The traditional method for determining compression index is consolidation test which is expensive and time consuming. Many researchers have used regression methods to develop empirical equations for predicting compression index from soil properties. Based on a large number of compression index data collected from consolidation tests, the accuracy of some popularly empirical equations were assessed. It was found that primary compression index is significantly overestimated in some equations while it is underestimated in others. The sensitivity analyses of soil parameters including water content, liquid limit and void ratio were performed. The results indicate that the compression index obtained from void ratio is most accurate. The ANOVA (analysis of variance) demonstrates that the equations with multiple soil parameters cannot provide better predictions than the equations with single soil parameter. In other words, it is not necessary to develop the relationships between compression index and multiple soil parameters. Meanwhile, it was noted that secondary compression index is approximately 0.7-5.0% of primary compression index with an average of 2.0%. In the end, the proposed prediction equations using power regression technique were provided that can provide more accurate predictions than those from existing equations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compression%20index" title="compression index">compression index</a>, <a href="https://publications.waset.org/abstracts/search?q=clay" title=" clay"> clay</a>, <a href="https://publications.waset.org/abstracts/search?q=settlement" title=" settlement"> settlement</a>, <a href="https://publications.waset.org/abstracts/search?q=consolidation" title=" consolidation"> consolidation</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20compression%20index" title=" secondary compression index"> secondary compression index</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20parameter" title=" soil parameter"> soil parameter</a> </p> <a href="https://publications.waset.org/abstracts/111582/compression-index-estimation-by-water-content-and-liquid-limit-and-void-ratio-using-statistics-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111582.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">163</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">1838</span> Multimodal Convolutional Neural Network for Musical Instrument Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yagya%20Raj%20Pandeya">Yagya Raj Pandeya</a>, <a href="https://publications.waset.org/abstracts/search?q=Joonwhoan%20Lee"> Joonwhoan Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimodal" title="multimodal">multimodal</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20convolution" title=" 3D convolution"> 3D convolution</a>, <a href="https://publications.waset.org/abstracts/search?q=music-video%20feature%20extraction" title=" music-video feature extraction"> music-video feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20mean" title=" generalized mean"> generalized mean</a> </p> <a href="https://publications.waset.org/abstracts/104041/multimodal-convolutional-neural-network-for-musical-instrument-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104041.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">215</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">1837</span> Surveillance Video Summarization Based on Histogram Differencing and Sum Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Jasim%20Habeeb">Nada Jasim Habeeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Saad%20Mohammed"> Rana Saad Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muntaha%20Khudair%20Abbass"> Muntaha Khudair Abbass </a> </p> <p class="card-text"><strong>Abstract:</strong></p> For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20differencing" title="temporal differencing">temporal differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title=" video summarization"> video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20differencing" title=" histogram differencing"> histogram differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20conditional%20variance" title=" sum conditional variance"> sum conditional variance</a> </p> <a href="https://publications.waset.org/abstracts/54404/surveillance-video-summarization-based-on-histogram-differencing-and-sum-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54404.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> <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=video%20compression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=video%20compression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=video%20compression&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=video%20compression&page=5">5</a></li> <li class="page-item"><a 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