Cited by 391Image and Video Quality AssessmentDigital Media Forensic DetectionImage Retrieval and Classification Techniques

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Quality Assessment of In-the-Wild Videos

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Li, Dingquan, Jiang, Tingting, Jiang, Ming·2019·Proceedings of the 27th ACM International Conference on Multimedia

DOI 10.1145/3343031.3351028

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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.

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Authors: Li, D., Jiang, T., & Jiang, M.
Affiliations: Peking University (CN)
Venue: Proceedings of the 27th ACM International Conference on Multimedia
Publisher: ACM
Published: 2019-10-15
Pages: 2351-2359
DOI: 10.1145/3343031.3351028
OpenAlex: W2965644659
Cited by: 387
References: 41
Citations by year: 2026: 44 · 2025: 80 · 2024: 76 · 2023: 76 · 2022: 52 · 2021: 49 · 2020: 10
FWCI: 9.05
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM International Conference on Multimedia (ACM MM) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: the National Basic Research Program of China (973 Program) (2015CB351803)
the Natural Science Foundation of China (61572042, 61520106004, 61527804)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords: no-reference video quality assessment, human visual system, temporal memory, gated recurrent unit, temporal pooling, long-range dependencies

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