{"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/opendmc-an-open-source-library-and","title":"OpenDMC: An Open-Source Library and Performance Evaluation for Deep-learning-based Multi-frame Compression","arxiv_id":null,"date":"2023-10-27","proceeding":"journal 2023 10","authors":["Wei Gao","Shangkun Sun","Huiming Zheng","Yuyang Wu","Hua Ye","Yongchi Zhang"],"abstract":"Video streaming has become an essential component of our everyday routines. Nevertheless, video data imposes a significant strain on data usage, demanding substantial bandwidth and storage resources for effective transmission. To suit explosively increasing video transmission and storage requirements, deep-learning-based video compression has developed rapidly in the past few years. New methods have mushroomed in order to achieve better Rate-Distortion (RD) performance. However, the absence of an algorithm library that can effectively sort, classify, and conduct extensive benchmark testing on existing algorithms remains a challenge. In this paper, we present an open-source algorithm library called OpenDMC, which integrates a variety of end-to-end video compression methods in cross-platform environments. We provide comprehensive descriptions of the algorithms used in the library, including their contributions and implementation details. We perform a thorough benchmarking test to evaluate the performance of the algorithms. We meticulously compare and analyze each algorithm based on various metrics, including RD performance, running time, and GPU memory usage.","url_abs":"https://dl.acm.org/doi/10.1145/3581783.3613464","url_pdf":"https://dl.acm.org/doi/10.1145/3581783.3613464","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":"opendmc-an-open-source-library-and","repo_url":"https://github.com/2024-MindSpore-1/Code10/tree/main/opendmc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"video-compression","task_name":"Video Compression"}],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}