{"id":6817,"date":"2012-01-04T00:54:02","date_gmt":"2012-01-03T22:54:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=6817"},"modified":"2012-01-04T00:54:02","modified_gmt":"2012-01-03T22:54:02","slug":"parallel-implementation-algorithm-of-motion-estimation-for-gpu-applications","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6817","title":{"rendered":"Parallel Implementation Algorithm of Motion Estimation for GPU Applications"},"content":{"rendered":"<p>The video coding standard H.264\/AVC can achieve higher coding efficiency than previous standards. However, it comes at the expense of an increased encoding complexity, especially for motion estimation process which induces very time consuming task even for current central processing units (CPU). On the other hand, due to the rapid growth of the processing capability of graphics processing unit (GPU), using GPU as a coprocessor to assist the CPU in computing massive data becomes essential. In this work, we propose a fast parallel algorithm for motion estimation (ME) process in H.264\/AVC on a computer unified device architecture (CUDA) platform. The proposed algorithm performs the parallel calculation of the residuals and SAD. Simulation results show that with the assistance of GPU the processing time is about 2 times faster than that of using CPU only.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The video coding standard H.264\/AVC can achieve higher coding efficiency than previous standards. However, it comes at the expense of an increased encoding complexity, especially for motion estimation process which induces very time consuming task even for current central processing units (CPU). On the other hand, due to the rapid growth of the processing capability [&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":[36,89,33,3],"tags":[1787,14,125,1786,20,251],"class_list":["post-6817","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-h-264avc","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-285"],"views":2033,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6817","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=6817"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6817\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6817"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6817"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6817"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}