Digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time-cost remains a challenging problem. This paper presents a highly parallel algorithm to boost the real-time application of the serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, but offers limited opportunity for GPU acceleration. In this paper, our method integrates the contrast-aware halftoning into a new parallelizable error-diffusion halftoning framework. To eliminate visually disturbing artifacts resulting from the parallelization, we propose a novel multiple quantization model and the space-filling curve to maintain the tone consistency, blue noise property and structure consistency. Our GPU implementation on a commodity PC platform achieves a real-time performance for a moderate-sized image. We demonstrate high quality and performance of the proposed approach with a variety of examples, and provide comparisons with the state-of-the-art methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
Digital image halftoning is a widely used technique. However, achieving high fidelity tone reproduction and structural preservation with low computational time-cost remains a challenging problem. This paper presents a highly parallel algorithm to boost the real-time application of the serial structure-preserving error diffusion. The contrast-aware halftoning approach is one such technique with superior structure preservation, […]<\/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":[36,89,33,3],"tags":[1787,14,1786,20,379],"class_list":["post-10938","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-480"],"views":2920,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10938","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=10938"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10938\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10938"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10938"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10938"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}