{"id":921,"date":"2010-10-27T13:47:58","date_gmt":"2010-10-27T13:47:58","guid":{"rendered":"http:\/\/hgpu.org\/?p=921"},"modified":"2010-10-27T13:47:58","modified_gmt":"2010-10-27T13:47:58","slug":"techniques-for-efficient-dctidct-implementation-on-generic-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=921","title":{"rendered":"Techniques for efficient DCT\/IDCT implementation on generic GPU"},"content":{"rendered":"<p>The emergence of programmable graphics processing units (GPU) has led to increasing interest in off-loading numerically intensive computations on to graphics hardware. DCT\/IDCT is widely adopted in modern image\/video compression standards and is usually one of the most computationally expensive parts. We present several techniques for efficient implementation of DCT\/IDCT on generic programmable GPU, using direct matrix multiplication. Our experimental results demonstrate that the speed of IDCT on a GPU using the proposed techniques can well exceed that on a CPU with MMX optimization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The emergence of programmable graphics processing units (GPU) has led to increasing interest in off-loading numerically intensive computations on to graphics hardware. DCT\/IDCT is widely adopted in modern image\/video compression standards and is usually one of the most computationally expensive parts. We present several techniques for efficient implementation of DCT\/IDCT on generic programmable GPU, using [&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":[11,33,3],"tags":[7,184,1782,114,1786],"class_list":["post-921","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-image-processing","category-paper","tag-ati","tag-ati-radeon-9700-pro","tag-computer-science","tag-hlsl","tag-image-processing"],"views":5129,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/921","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=921"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/921\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}