{"id":14404,"date":"2015-08-10T22:39:04","date_gmt":"2015-08-10T19:39:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=14404"},"modified":"2015-08-10T22:39:04","modified_gmt":"2015-08-10T19:39:04","slug":"accelerating-the-pre-processing-stages-of-jpeg-encoder-on-a-heterogenous-system-using-opencl","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=14404","title":{"rendered":"Accelerating the pre-processing stages of JPEG encoder on a heterogenous system using OpenCL"},"content":{"rendered":"<p>Color space conversion and downsampling are among the major computationally intensive steps in typical image and video codec standards, and accelerating these steps will improve the performances of these applications significantly. In this paper, we describe the parallel implementation of the color space conversion and downsampling as pre-processing steps for the JPEG encoder in a heterogeneous environment using the most recent cross-platform Open Computing Language (OpenCL). This work combines a multi-core CPU and a many-core GPU in a single solution to perform the computation of the JPEG encoder pre-processing stages. In comparing with CPU-based implementation, our OpenCL parallel implementation results in an increase in the speed of the computations by factors of 8.78 on both CPU and GPU devices.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Color space conversion and downsampling are among the major computationally intensive steps in typical image and video codec standards, and accelerating these steps will improve the performances of these applications significantly. In this paper, we describe the parallel implementation of the color space conversion and downsampling as pre-processing steps for the JPEG encoder in a [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[33,90,3],"tags":[7,1188,452,1786,1793],"class_list":["post-14404","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-6850","tag-heterogeneous-systems","tag-image-processing","tag-opencl"],"views":2260,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14404","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=14404"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14404\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}