{"id":8597,"date":"2012-12-03T23:39:33","date_gmt":"2012-12-03T21:39:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=8597"},"modified":"2012-12-03T23:39:33","modified_gmt":"2012-12-03T21:39:33","slug":"gpu-based-implementation-of-jpeg2000-encoder","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8597","title":{"rendered":"GPU-Based Implementation of JPEG2000 Encoder"},"content":{"rendered":"<p>JPEG2000 has become one of the most rewarding image coding standards. It provides a practical set of features which weren&#8217;t necessarily available in the previous standards. The features were realized as a result of two new techniques, namely the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). The complexity of EBCOT Tier-1 makes its implementations very difficult and time consuming. In this paper, we focus on accelerating JPEG2000 encoder by using general-purpose processing on Graphical Processing Unit (GPU). We use CUDA platform to implement DWT and EBCOT Tier-1 as the most important sections of JPEG2000. Resulting implementation of proposed architecture performs very well compared to other available implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>JPEG2000 has become one of the most rewarding image coding standards. It provides a practical set of features which weren&#8217;t necessarily available in the previous standards. The features were realized as a result of two new techniques, namely the Discrete Wavelet Transform (DWT), and Embedded Block Coding with Optimized Truncation (EBCOT). The complexity of EBCOT [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,33,3],"tags":[14,362,1786,364,20,379],"class_list":["post-8597","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-discrete-wavelet-transform","tag-image-processing","tag-jpeg2000","tag-nvidia","tag-nvidia-geforce-gtx-480"],"views":4051,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8597","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8597"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8597\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8597"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8597"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}