{"id":3009808,"date":"2025-07-10T08:00:00","date_gmt":"2025-07-10T15:00:00","guid":{"rendered":"urn:uuid:83503638-02d0-4f2f-8d4f-d7ad09b63ef8"},"modified":"2025-07-09T10:52:32","modified_gmt":"2025-07-09T17:52:32","slug":"perceptual-video-compression-with-neural-wrapping","status":"publish","type":"research-post","link":"https:\/\/sonyinteractive.com\/en\/innovation\/research-academia\/research\/perceptual-video-compression-with-neural-wrapping\/","title":{"rendered":"Perceptual Video Compression with Neural Wrapping"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:70%\">\t\t<div\n\t\t\tclass=\"wp-block-sie-social-share social-share--raw\"\n\t\t\tdata-aa-modulename=\"social-share\"\n\t\t\tdata-url=\"https:\/\/sonyinteractive.com\/en\/innovation\/research-academia\/research\/perceptual-video-compression-with-neural-wrapping\/\"\n\t\t\tdata-title=\"Perceptual Video Compression with Neural Wrapping\"\n\t\t\tdata-heading=\"Share this\"\n\t\t\tdata-bluesky-title=\"\"\n\t\t\tdata-twitter-title=\"\"\n\t\t\tdata-twitter-hashtags=\"\"\n\t\t\tdata-reddit-title=\"\"\n\t\t\tdata-email-subject=\"\"\n\t\t\tdata-email-body=\"\"\n\t\t\t>\n\t\t<\/div>\n\t\n\n<div class=\"post-author\" data-no-of-bylines=\"4\"><ul><li>Yiannis Andreopoulos<span class=\"sie-style-body-small\">Director, Machine Learning, Sony Interactive Entertainment<\/span><\/li><li>Umar Khan<span class=\"sie-style-body-small\">Senior Machine Learning Engineer, Sony Interactive Entertainment<\/span><\/li><li>Aaron Chadha<span class=\"sie-style-body-small\">Principal Machine Learning Engineer, Sony Interactive Entertainment<\/span><\/li><li>Anam Russell<span class=\"sie-style-body-small\">Senior Software Engineer, Sony Interactive Entertainment<\/span><\/li><\/ul><\/div>\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" width=\"2424\" height=\"600\" decoding=\"async\" src=\"https:\/\/sonyinteractive.com\/tachyon\/2025\/07\/fig_visual.png\" alt=\"\" class=\"wp-image-3009809\" srcset=\"https:\/\/sonyinteractive.com\/tachyon\/2025\/07\/fig_visual.png?resize=2424%2C600&amp;zoom=1 2424w, https:\/\/sonyinteractive.com\/tachyon\/2025\/07\/fig_visual.png?resize=2424%2C600&amp;zoom=0.5 1212w, https:\/\/sonyinteractive.com\/tachyon\/2025\/07\/fig_visual.png?resize=2424%2C600&amp;zoom=0.25 606w\" sizes=\"auto, (max-width: 2424px) 100vw, 2424px\" \/><\/figure>\n\n\n\n<p class=\"sie-paragraph\">Standard video codecs are rate-distortion optimization machines, where distortion is typically quantified using PSNR versus the source. However, it is now widely accepted that increasing PSNR does not necessarily translate to better visual quality. In this paper, a better balance between perception and fidelity is targeted, in order to provide for significant rate savings over state-of-the-art standards-based video codecs. Specifically, pre- and postprocessing neural networks are proposed that enhance the coding efficiency of standard video codecs when benchmarked with an array of well-established perceptual quality scores. These \u201cneural wrapper\u201d elements are end-to-end trained with a neural codec module serving as a differentiable proxy for standard video codecs. The codec proxy is jointly optimized with the pre- and post components via a novel two-phase pretraining strategy and end-to-end iterative refinement with stop-gradient. This allows the neural pre- and postprocessor to learn to embed, remove and recover information in a codec-aware manner, thus improving its rate-quality performance. A single neural-wrapper model is thereby established and used for the entire ratequality curve without needing any downscaling or upscaling. The trained model is tested with the AV1 and VVC standard codecs via an array of well-established objective quality scores (SSIM, MS-SSIM, VMAF, AVQT), as well as mean opinion scores (MOS) derived from ITU-T P.910 subjective testing. Experimental results show that the proposed approach improves all quality scores, with -18.5% average Bjontegaard Delta-rate (BD-rate) saving over all objective scores and MOS improvement over both standard codecs. This illustrates the significant potential of neural wrapper components over standards-based video coding.<\/p>\n\n\n\n<p class=\"sie-paragraph\">Read the full paper here: <a class=\"sie-paragraph\" href=\"https:\/\/openaccess.thecvf.com\/content\/CVPR2025\/papers\/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/openaccess.thecvf.com\/content\/CVPR2025\/papers\/Khan_Perceptual_Video_Compression_with_Neural_Wrapping_CVPR_2025_paper.pdf<\/a><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n\t\t<div class=\"related-posts__container\" data-selection-type=\"latest\" data-post-type=\"research-post\">\n\t\t\t<h2 class=\"related-posts__heading sie-style-h5\">Latest Research Posts<\/h2>\n\t\t\t<div class=\"related-posts related-posts--vertical\">\n\t\t\t\t<article class=\"related-post related-post--research-post\"><div class=\"related-post__content\"><h3 class=\"related-post__title sie-style-body-small-v2\">\n\t\t<a href=\"https:\/\/sonyinteractive.com\/en\/innovation\/research-academia\/research\/perceptually-guided-3dgs-streaming-and-rendering-for-mixed-reality\/\">\n\t\t\tPerceptually Guided 3DGS Streaming and Rendering for Mixed Reality\n\t\t<\/a>\n\t<\/h3><\/div><\/article><article class=\"related-post related-post--research-post\"><div class=\"related-post__content\"><h3 class=\"related-post__title sie-style-body-small-v2\">\n\t\t<a href=\"https:\/\/sonyinteractive.com\/en\/innovation\/research-academia\/research\/content-adaptive-encoding-for-interactive-game-streaming\/\">\n\t\t\tContent Adaptive Encoding for Interactive Game Streaming\n\t\t<\/a>\n\t<\/h3><\/div><\/article><article class=\"related-post related-post--research-post\"><div class=\"related-post__content\"><h3 class=\"related-post__title sie-style-body-small-v2\">\n\t\t<a href=\"https:\/\/sonyinteractive.com\/en\/innovation\/research-academia\/research\/vision-language-models-for-quality-assurance\/\">\n\t\t\tVideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance\n\t\t<\/a>\n\t<\/h3><\/div><\/article>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-sie-scroll-to-top\" data-aa-modulename=\"sie-scroll-to-top\"><button class=\"sie-btn sie-btn--action\" type=\"button\"><span>Back to top<\/span><\/button><\/div>\n","protected":false},"author":34,"parent":0,"template":"","byline":[312,313,314,315],"research-post-category":[199],"class_list":["post-3009808","research-post","type-research-post","status-publish","hentry","research-post-category-impact","post-perceptual-video-compression-with-neural-wrapping"],"ab_tests":{},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - 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