{"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/no-reference-video-quality-assessment-using-1","title":"No-Reference Video Quality Assessment Using Space-Time Chips","arxiv_id":"2008.00031","date":"2020-08-23","proceeding":null,"authors":[],"abstract":"We propose a new prototype model for no-reference video quality assessment\n(VQA) based on the natural statistics of space-time chips of videos. Space-time\nchips (ST-chips) are a new, quality-aware feature space which we define as\nspace-time localized cuts of video data in directions that are determined by\nthe local motion flow. We use parametrized distribution fits to the bandpass\nhistograms of space-time chips to characterize quality, and show that the\nparameters from these models are affected by distortion and can hence be used\nto objectively predict the quality of videos. Our prototype method, which we\ncall ChipQA-0, is agnostic to the types of distortion affecting the video, and\nis based on identifying and quantifying deviations from the expected statistics\nof natural, undistorted ST-chips in order to predict video quality. We train\nand test our resulting model on several large VQA databases and show that our\nmodel achieves high correlation against human judgments of video quality and is\ncompetitive with state-of-the-art models.","url_abs":"http://arxiv.org/abs/2008.00031v3","url_pdf":"http://arxiv.org/pdf/2008.00031v3.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":"no-reference-video-quality-assessment-using-1","repo_url":"https://github.com/JoshuaEbenezer/ChipQA-0","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-quality-assessment-on-live-livestream","task":"Video Quality Assessment","dataset":"LIVE Livestream","model":"ChipQA-0","rank_in_archive_order":2,"of":4,"metrics":{"SRCC":"0.7513"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-live-etri","task":"Video Quality Assessment","dataset":"LIVE-ETRI","model":"ChipQA-0","rank_in_archive_order":5,"of":7,"metrics":{"SRCC":"0.4028"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}