{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dvc-an-end-to-end-deep-video-compression","title":"DVC: An End-to-end Deep Video Compression Framework","arxiv_id":"1812.00101","date":"2018-11-30","proceeding":"CVPR 2019 6","authors":["Guo Lu","Wanli Ouyang","Dong Xu","Xiaoyun Zhang","Chunlei Cai","Zhiyong Gao"],"abstract":"Conventional video compression approaches use the predictive coding\narchitecture and encode the corresponding motion information and residual\ninformation. In this paper, taking advantage of both classical architecture in\nthe conventional video compression method and the powerful non-linear\nrepresentation ability of neural networks, we propose the first end-to-end\nvideo compression deep model that jointly optimizes all the components for\nvideo compression. Specifically, learning based optical flow estimation is\nutilized to obtain the motion information and reconstruct the current frames.\nThen we employ two auto-encoder style neural networks to compress the\ncorresponding motion and residual information. All the modules are jointly\nlearned through a single loss function, in which they collaborate with each\nother by considering the trade-off between reducing the number of compression\nbits and improving quality of the decoded video. Experimental results show that\nthe proposed approach can outperform the widely used video coding standard\nH.264 in terms of PSNR and be even on par with the latest standard H.265 in\nterms of MS-SSIM. Code is released at https://github.com/GuoLusjtu/DVC.","url_abs":"http://arxiv.org/abs/1812.00101v3","url_pdf":"http://arxiv.org/pdf/1812.00101v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dvc-an-end-to-end-deep-video-compression","repo_url":"https://github.com/GuoLusjtu/DVC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"dvc-an-end-to-end-deep-video-compression","repo_url":"https://github.com/SaipingZhang/DVC_P","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"dvc-an-end-to-end-deep-video-compression","repo_url":"https://github.com/binzzheng/DVC-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"dvc-an-end-to-end-deep-video-compression","repo_url":"https://github.com/zhihaohu/pytorchvideocompression","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"ms-ssim","task_name":"MS-SSIM"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"video-compression","task_name":"Video Compression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.00101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00101"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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