{"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/quality-assessment-of-in-the-wild-videos","title":"Quality Assessment of In-the-Wild Videos","arxiv_id":"1908.00375","date":"2019-08-01","proceeding":null,"authors":["Dingquan Li","Tingting Jiang","Ming Jiang"],"abstract":"Quality assessment of in-the-wild videos is a challenging problem because of the absence of reference videos and shooting distortions. Knowledge of the human visual system can help establish methods for objective quality assessment of in-the-wild videos. In this work, we show two eminent effects of the human visual system, namely, content-dependency and temporal-memory effects, could be used for this purpose. We propose an objective no-reference video quality assessment method by integrating both effects into a deep neural network. For content-dependency, we extract features from a pre-trained image classification neural network for its inherent content-aware property. For temporal-memory effects, long-term dependencies, especially the temporal hysteresis, are integrated into the network with a gated recurrent unit and a subjectively-inspired temporal pooling layer. To validate the performance of our method, experiments are conducted on three publicly available in-the-wild video quality assessment databases: KoNViD-1k, CVD2014, and LIVE-Qualcomm, respectively. Experimental results demonstrate that our proposed method outperforms five state-of-the-art methods by a large margin, specifically, 12.39%, 15.71%, 15.45%, and 18.09% overall performance improvements over the second-best method VBLIINDS, in terms of SROCC, KROCC, PLCC and RMSE, respectively. Moreover, the ablation study verifies the crucial role of both the content-aware features and the modeling of temporal-memory effects. The PyTorch implementation of our method is released at https://github.com/lidq92/VSFA.","url_abs":"https://arxiv.org/abs/1908.00375v3","url_pdf":"https://arxiv.org/pdf/1908.00375v3.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":"quality-assessment-of-in-the-wild-videos","repo_url":"https://github.com/lidq92/VSFA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"quality-assessment-of-in-the-wild-videos","repo_url":"https://github.com/SpikeKing/VQA-v2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-quality-assessment-on-konvid-1k","task":"Video Quality Assessment","dataset":"KoNViD-1k","model":"VSFA","rank_in_archive_order":18,"of":21,"metrics":{"PLCC":"0.7754"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-live-vqc","task":"Video Quality Assessment","dataset":"LIVE-VQC","model":"VSFA","rank_in_archive_order":19,"of":20,"metrics":{"PLCC":"0.7426"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"VSFA","rank_in_archive_order":6,"of":21,"metrics":{"KLCC":"0.7483","PLCC":"0.9180","SRCC":"0.9049","Type":"NR"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-sr-qa-dataset","task":"Video Quality Assessment","dataset":"MSU SR-QA Dataset","model":"VSFA","rank_in_archive_order":29,"of":60,"metrics":{"KLCC":"0.43634","PLCC":"0.54407","SROCC":"0.53652","Type":"NR"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1908.00375","atlas_url":"https://app.syntology.ai/?focus=1908.00375","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.00375"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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