{"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/disentangling-aesthetic-and-technical-effects","title":"Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives","arxiv_id":"2211.04894","date":"2022-11-09","proceeding":"ICCV 2023 1","authors":["HaoNing Wu","Erli Zhang","Liang Liao","Chaofeng Chen","Jingwen Hou","Annan Wang","Wenxiu Sun","Qiong Yan","Weisi Lin"],"abstract":"The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA problem is still ambiguous and can be viewed from two perspectives: the technical perspective, measuring the perception of distortions; and the aesthetic perspective, which relates to preference and recommendation on contents. To understand how these two perspectives affect overall subjective opinions in UGC-VQA, we conduct a large-scale subjective study to collect human quality opinions on overall quality of videos as well as perceptions from aesthetic and technical perspectives. The collected Disentangled Video Quality Database (DIVIDE-3k) confirms that human quality opinions on UGC videos are universally and inevitably affected by both aesthetic and technical perspectives. In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives. The DOVER proves state-of-the-art performance in UGC-VQA under very high efficiency. With perspective opinions in DIVIDE-3k, we further propose DOVER++, the first approach to provide reliable clear-cut quality evaluations from a single aesthetic or technical perspective. Code at https://github.com/VQAssessment/DOVER.","url_abs":"https://arxiv.org/abs/2211.04894v3","url_pdf":"https://arxiv.org/pdf/2211.04894v3.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":"disentangling-aesthetic-and-technical-effects","repo_url":"https://github.com/vqassessment/dover","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"disentangling-aesthetic-and-technical-effects","repo_url":"https://github.com/QualityAssessment/DOVER","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"disentangling-aesthetic-and-technical-effects","repo_url":"https://github.com/VQAssessment/FAST-VQA-and-FasterVQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"disentanglement","task_name":"Disentanglement"},{"task_slug":"video-generation","task_name":"Video Generation"},{"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-konvid-1k","task":"Video Quality Assessment","dataset":"KoNViD-1k","model":"DOVER (end-to-end)","rank_in_archive_order":1,"of":21,"metrics":{"PLCC":"0.905"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-konvid-1k","task":"Video Quality Assessment","dataset":"KoNViD-1k","model":"DOVER (head-only)","rank_in_archive_order":3,"of":21,"metrics":{"PLCC":"0.894"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-live-fb-lsvq","task":"Video Quality Assessment","dataset":"LIVE-FB LSVQ","model":"DOVER","rank_in_archive_order":2,"of":13,"metrics":{"PLCC":"0.889"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-live-vqc","task":"Video Quality Assessment","dataset":"LIVE-VQC","model":"DOVER (end-to-end)","rank_in_archive_order":2,"of":20,"metrics":{"PLCC":"0.874"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-live-vqc","task":"Video Quality Assessment","dataset":"LIVE-VQC","model":"DOVER (head-only)","rank_in_archive_order":3,"of":20,"metrics":{"PLCC":"0.863"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"DOVER","rank_in_archive_order":8,"of":21,"metrics":{"KLCC":"0.7216","PLCC":"0.9099","SRCC":"0.8871","Type":"NR"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-youtube-ugc","task":"Video Quality Assessment","dataset":"YouTube-UGC","model":"DOVER (end-to-end)","rank_in_archive_order":1,"of":17,"metrics":{"PLCC":"0.874"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-youtube-ugc","task":"Video Quality Assessment","dataset":"YouTube-UGC","model":"DOVER (head-only)","rank_in_archive_order":3,"of":17,"metrics":{"PLCC":"0.862"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.04894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.04894"}},"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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