A recently proposed approach, called seam carving, has been widely used for content-aware resizing of images and videos with little to no perceptible distortion. Unfortunately, for high-resolution videos and large images it is not computationally feasible to do the resizing in real-time using small-scale CPU systems. In this paper, we exploit highly parallel computational capabilities of CUDA-enabled Graphics Processing Units (GPUs) in a heterogeneous computer system for accelerating the content-aware resizing of videos and images. The performance results show that our implementation of the seam carving algorithm achieves up to 235x and 30x speed-ups on the computationally-intensive part of the algorithm compared to the single-threaded and the multithreaded CPU implementations, respectively, on the systems tested. The overall resizing operation is up to 7x and 4x faster than the single-threaded and multithreaded CPU implementations, respectively, which demonstrates the potential to resize videos and large images in real-time.<\/p>\n","protected":false},"excerpt":{"rendered":"
A recently proposed approach, called seam carving, has been widely used for content-aware resizing of images and videos with little to no perceptible distortion. Unfortunately, for high-resolution videos and large images it is not computationally feasible to do the resizing in real-time using small-scale CPU systems. In this paper, we exploit highly parallel computational capabilities […]<\/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":[89,33,3],"tags":[14,1786,20,226,974],"class_list":["post-10541","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-geforce-8800-gt","tag-nvidia-geforce-gtx-580"],"views":3626,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10541","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=10541"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10541\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10541"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10541"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10541"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}