{"id":3736,"date":"2011-04-28T20:57:37","date_gmt":"2011-04-28T20:57:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=3736"},"modified":"2011-04-28T20:57:37","modified_gmt":"2011-04-28T20:57:37","slug":"multi-pass-and-frame-parallel-algorithms-of-motion-estimation-in-h-264avc-for-generic-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3736","title":{"rendered":"Multi-Pass and Frame Parallel Algorithms of Motion Estimation in H.264\/AVC for Generic GPU"},"content":{"rendered":"<p>In this paper, multi-pass and frame parallel algorithms are proposed to accelerate various motion estimation (ME) tools in H.264 with the graphics processing unit (GPU). By the multi-pass method to unroll and rearrange the multiple nested loops, the integer-pel ME can be implemented with two-pass process on GPU. Moreover, fractional ME needs six passes for frame interpolation with six-tap filter and motion vector refinement. Motion estimation with multiple reference frames can be implemented with two-pass process with frame-level parallel scheme by use of SIMD vector operations of GPU. Experimental results show that, compared to implementations with only CPU, about 6 times to 56 times speed-up can be achieved for different ME algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, multi-pass and frame parallel algorithms are proposed to accelerate various motion estimation (ME) tools in H.264 with the graphics processing unit (GPU). By the multi-pass method to unroll and rearrange the multiple nested loops, the integer-pel ME can be implemented with two-pass process on GPU. Moreover, fractional ME needs six passes for [&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":[33,3],"tags":[125,1786,35],"class_list":["post-3736","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-paper","tag-h-264avc","tag-image-processing","tag-video-decoding"],"views":2035,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3736","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=3736"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3736\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}