{"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/video-compression-dataset-and-benchmark-of","title":"Video compression dataset and benchmark of learning-based video-quality metrics","arxiv_id":"2211.12109","date":"2022-11-22","proceeding":"NeurIPS 2022 11","authors":["Anastasia Antsiferova","Sergey Lavrushkin","Maksim Smirnov","Alexander Gushchin","Dmitriy Vatolin","Dmitriy Kulikov"],"abstract":"Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ videos compressed using older standards, such as AVC. In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of about 2,500 streams encoded using different standards, including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using crowdsourced pairwise comparisons. The list of evaluated metrics includes recent ones based on machine learning and neural networks. The results demonstrate that new no-reference metrics exhibit a high correlation with subjective quality and approach the capability of top full-reference metrics.","url_abs":"https://arxiv.org/abs/2211.12109v2","url_pdf":"https://arxiv.org/pdf/2211.12109v2.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":"video-compression-dataset-and-benchmark-of","repo_url":"https://github.com/msu-video-group/msu_vqm_compression_benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"video-compression","task_name":"Video Compression"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"}],"methods":[{"method_slug":"mdtvsfa","method_name":"MDTVSFA"},{"method_slug":"nima","method_name":"NIMA"}],"datasets_introduced":[{"slug":"msu-video-quality-metrics-dataset","name":"MSU FR VQA Database","full_name":"MSU Full-Reference Video Quality Assessment Database"},{"slug":"msu-video-quality-metrics-benchmark","name":"MSU NR VQA Database","full_name":"MSU No-Reference Video Quality Assessment Database"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.12109","atlas_url":"https://app.syntology.ai/?focus=2211.12109","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}