In the last few years a myriad of computer graphic applications have been developed using standard programming techniques, which are mainly based on multicore general-purpose processors (CPUs) architectures. Due to the rapid turning towards high definition multimedia, more and more researches have been done that need both computational resources and memory space to achieve high performance. To this end, more recently the general-purpose computing on graphic processing units (GPGPUs) architectures, which are becoming increasingly programmable and scalable, have been adopted. Since the GPUs provide a vast number of simple, data-parallel, deeply multithreaded cores and high memory bandwidths, they have been used to provide a valid support in several fields such as image and video processing research area, medical and human applications. In this paper the most recent image and video processing techniques and relevant applications are reviewed, highlighting the advantages of using CUDA in terms of efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"
In the last few years a myriad of computer graphic applications have been developed using standard programming techniques, which are mainly based on multicore general-purpose processors (CPUs) architectures. Due to the rapid turning towards high definition multimedia, more and more researches have been done that need both computational resources and memory space to achieve high […]<\/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],"class_list":["post-6445","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-nvidia"],"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\/6445","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=6445"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6445\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6445"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6445"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6445"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}