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Quality Assessment of In-the-Wild Videos
Open accessAbstract
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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“These SOTA deep CNN-based BVQA models [71][74] produce accurate quality predictions on legacy (single synthetic distortion) video datasets [1], [6], but struggle on recent in-the-wild UGC databases [10], [12], [13].”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [74]
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A Quality-of-Experience Database for Adaptive Omnidirectional Video Streaming
Liu, X., Yan, J., Wan, Z., Fang, Y., & Wang, Z. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2023.3300529 · Cited by 6
A Perspective on Quality Evaluation for AI-Generated Videos
Zhang, Z., Sun, W., & Zhai, G. (2025) · Sensors · doi:10.3390/s25154668 · Cited by 5
Semi-Supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
Mitra, S., Jogani, S., & Soundararajan, R. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3347090 · Cited by 5
Zhang, X., & Li, L. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30351 · Cited by 5
TQP: An Efficient Video Quality Assessment Framework for Adaptive Bitrate Video Streaming
Aslam, M. A., Xu, W. Z., Ahmed, N., Saleem, G., Shuangtong, Z., Xu, Y., & Hu, H. (2024) · IEEE Access · doi:10.1109/access.2024.3418375 · Cited by 5
No-reference video quality assessment based on human visual perception
Zhou, Z., Kong, G., Duan, X., & Long, H. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.4.043029 · Cited by 5
A Review of Learning-Based Methods for Adaptive Video Streaming Over HTTP
Amer, H., Hassan, M. S., & Ismail, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3582850 · Cited by 5
Deep Blind Video Quality Assessment for User Generated Videos
Tang, J., Dong, Y., Xie, R., Gu, X., Song, L., Li, L., & Zhou, B. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301757 · Cited by 5
Hybrid No-Reference Quality Assessment for Surveillance Images
Ye, Z., Ye, X., & Zhao, Z. (2022) · Information · doi:10.3390/info13120588 · Cited by 5
Inverse-tone-mapped HDR video quality assessment: A new dataset and benchmark
Fan, L., Yang, A., Guo, C., Jiang, X., Gao, Y., & Min, X. (2023) · Displays · doi:10.1016/j.displa.2023.102510 · Cited by 5
Integrates Spatiotemporal Visual Stimuli for Video Quality Assessment
Guo, W., Zhang, K., & Ke, X. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2023.3312932 · Cited by 5
Revisiting the robustness of spatio-temporal modeling in video quality assessment
Yan, J., Wu, L., Jiang, W., Liu, C., & Shen, F. (2023) · Displays · doi:10.1016/j.displa.2023.102585 · Cited by 5
A Database and Model for the Visual Quality Assessment of Super-Resolution Videos
Zhou, F., Wei, S., Lu, Z., & Qiu, G. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2024.3382949 · Cited by 7
Zhou, K., Hao, Z., Wang, L., & Liang, X. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3549179 · Cited by 5
Sun, W., Cao, L., Fu, K., Zhu, D., Jia, J., Hu, M., Min, X., & Zhai, G. (2025) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew68306.2025.11152231 · Cited by 5
An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM
Wen, W., Wang, Y., Birkbeck, N., & Adsumilli, B. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888524 · Cited by 4
Blind image quality assessment via aesthetic dataset construction and hierarchical feature fusion
Dong, W., Yin, H., Zhao, S., Ming, R., Wang, X., Huang, X., & Wang, H. (2025) · Displays · doi:10.1016/j.displa.2025.103065 · Cited by 4
MV-VVQA: Multi-View Learning for No-Reference Volumetric Video Quality Assessment
Yu, F., Zhang, Z., Sun, W., Min, X., Lin, J., Zhai, G., & Liu, N. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10290018 · Cited by 4
Efficient Computational Cost Saving in Video Processing for QoE Estimation
Llorente, Á., Pérez, J., Rodrigo, J. A., Jiménez, D., & Menéndez, J. M. (2024) · IEEE Access · doi:10.1109/access.2024.3373193 · Cited by 4
Liu, Z., Zeng, H., Chen, J., Ding, R., Shi, Y., & Hou, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3469180 · Cited by 4
Deep Learning-Based Approach to Evaluate the Quality of Corporate Brand Packaging Visual Design
Shi, D., Song, Z., & Huang, C. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425403456 · Cited by 4
Metadata
- 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
- 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
- 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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