{"id":3794,"date":"2011-05-04T11:51:21","date_gmt":"2011-05-04T11:51:21","guid":{"rendered":"http:\/\/hgpu.org\/?p=3794"},"modified":"2011-05-04T11:51:21","modified_gmt":"2011-05-04T11:51:21","slug":"efficient-parallel-intra-prediction-mode-selection-scheme-for-4x4-blocks-in-h-264","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3794","title":{"rendered":"Efficient Parallel Intra-prediction Mode Selection Scheme for 4&#215;4 Blocks in H.264"},"content":{"rendered":"<p>An intra-prediction mode with 4&#215;4 block and 16&#215;16 block sizes for luma component and 8&#215;8 block size for chroma component is used in H.264 to improve the rate-distortion performance. However, the computational complexity of H.264 encoder is drastically increased due to the various intraprediction modes. Recently efficient hardware architectures were proposed for the fast execution of H.264\/AVC intraprediction mode selection. This paper proposes an efficient pipelining method for the 4&#215;4 blocks intra-prediction mode selection. In particular, we exploit the GPU&#8217;s streaming architecture at 4 x 4 intra-prediction mode selection in H.264\/AVC and we develop a special strategy including instruction optimization and taking full advantage of shared memory to further exploit the fine-grained parallelism of GPUs. Experimental results up to about 3xspeedup of our GPU-based algorithms over the implementations on sequential CPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An intra-prediction mode with 4&#215;4 block and 16&#215;16 block sizes for luma component and 8&#215;8 block size for chroma component is used in H.264 to improve the rate-distortion performance. However, the computational complexity of H.264 encoder is drastically increased due to the various intraprediction modes. Recently efficient hardware architectures were proposed for the fast execution [&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":[36,33,3],"tags":[1787,125,1786,35],"class_list":["post-3794","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-image-processing","category-paper","tag-algorithms","tag-h-264avc","tag-image-processing","tag-video-decoding"],"views":2036,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3794","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=3794"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3794\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}