Due to its object based nature, flexible features and provision for user interaction, MPEG-4 encoder is highly suitable for parallelization. The most critical and time-consuming operation of encoder is motion estimation. Nvidia’s general-purpose graphical processing unit (GPGPU) architecture allows for a massively parallel stream processor model at a very cheap price (in a few thousands Rupees). However synchronization of parallel calculations and repeated device to host data transfer is a major challenge in parallelizing motion estimation on CUDA. Our solution employs optimized and balanced parallelization of motion estimation on CUDA. This paper discusses about frame-based parallelization wherein parallelization is done at two levels – at macroblock level and at search range level. We propose a further division of macroblock to optimize parallelization. Our algorithm supports real-time processing and streaming for key applications such as e-learning, telemedicine and video-surveillance systems, as demonstrated by experimental results.<\/p>\n","protected":false},"excerpt":{"rendered":"
Due to its object based nature, flexible features and provision for user interaction, MPEG-4 encoder is highly suitable for parallelization. The most critical and time-consuming operation of encoder is motion estimation. Nvidia’s general-purpose graphical processing unit (GPGPU) architecture allows for a massively parallel stream processor model at a very cheap price (in a few thousands […]<\/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":[14,1786,20,1012,35],"class_list":["post-3134","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-quadro-fx-370","tag-video-decoding"],"views":2332,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3134","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=3134"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3134\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}