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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SOTA deep CNN-based BVQA models produce accurate quality predictions on legacy (single synthetic distortion) video datasets but struggle on recent in-the-wild UGC databases.
“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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Subjective and Objective Quality of Experience of Free Viewpoint Videos
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Self-Supervised Representation Learning for Video Quality Assessment
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On the Content Bias in Fréchet Video Distance
Ge, S., Mahapatra, A., Parmar, G., Zhu, J.-Y., & Huang, J.-B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00695 · Cited by 31 · see citing works
COVER: A Comprehensive Video Quality Evaluator
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Completely Blind Quality Assessment of User Generated Video Content
Kancharla, P., & Channappayya, S. S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3130541 · Cited by 31 · see citing works
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment
Zhao, K., Yuan, K., Sun, M. B., & Wen, X. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00137 · Cited by 29 · see citing works
VDPVE: VQA Dataset for Perceptual Video Enhancement
Gao, Y., Cao, Y., Kou, T., Sun, W., Dong, Y., Liu, X., Min, X., & Zhai, G. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00152 · Cited by 29 · see citing works
Exploring Opinion-Unaware Video Quality Assessment with Semantic Affinity Criterion
Wu, H., Liao, L., Hou, J., Chen, C., Zhang, E., Wang, A., Sun, W., Yan, Q., & Lin, W. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00070 · Cited by 28 · see citing works
DDH-QA: A Dynamic Digital Humans Quality Assessment Database
Zhang, Z., Zhou, Y., Sun, W., Lu, W. J., Min, X., Wang, Y., & Zhai, G. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00429 · Cited by 28 · see citing works
LMM-VQA: Advancing Video Quality Assessment With Large Multimodal Models
Ge, Q., Sun, W., Zhang, Y., Li, Y., Ji, Z., Sun, F., Jui, S., Min, X., & Zhai, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3571788 · Cited by 32 · see citing works
Quality Assessment of UGC Videos Based on Decomposition and Recomposition
Liu, Y., Wu, J., Li, L., Dong, W., & Shi, G. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3209007 · Cited by 28 · see citing works
Temporal Reasoning Guided QoE Evaluation for Mobile Live Video Broadcasting
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Contrastive distortion‐level learning‐based no‐reference image‐quality assessment
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PVBLiF: A Pseudo Video-Based Blind Quality Assessment Metric for Light Field Image
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Perceptual Quality Assessment of Omnidirectional Images: A Benchmark and Computational Model
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Starvqa: Space-Time Attention for Video Quality Assessment
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StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
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Wang, J., Duan, H., Zhai, G., Wang, J., & Min, X. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01758 · Cited by 24 · see citing works
Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement
Dong, Y., Liu, X., Gao, Y., Zhou, X., Tan, T., & Zhai, G. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611923 · Cited by 24 · see citing works
FAVER: Blind quality prediction of variable frame rate videos
Zheng, Q., Tu, Z., Madhusudana, P. C., Zeng, X., Bovik, A. C., & Fan, Y. (2024) · Signal Processing Image Communication · doi:10.1016/j.image.2024.117101 · Cited by 24 · see citing works
A Spatial–Temporal Video Quality Assessment Method via Comprehensive HVS Simulation
Zhang, A., Wang, Y., Tang, W., Li, L., & Kwong, S. (2023) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2023.3338615 · Cited by 33 · see citing works
Completely Blind Quality Assessment of User Generated Video Content
Kancharla, P., & Channappayya, S. S. (2022) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 23 · see citing works
Subjective and Objective Analysis of Streamed Gaming Videos
Yu, X., Ying, Z., Birkbeck, N., Wang, Y., Adsumilli, B., & Bovik, A. C. (2023) · IEEE Transactions on Games · doi:10.1109/tg.2023.3293093 · Cited by 23 · see citing works
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
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Study of Spatio-Temporal Modeling in Video Quality Assessment
Fang, Y., Li, Z., Yan, J., Sui, X., & Liu, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3272480 · Cited by 23 · see citing works
CLIPVQA: Video Quality Assessment via CLIP
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A Perceptual Quality Metric for Video Frame Interpolation
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A no-reference quality assessment metric for dynamic 3D digital human
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No-Reference Video Quality Assessment Using Local Structural and Quality-Aware Deep Features
Vishwakarma, A. K., & Bhurchandi, K. M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3273654 · Cited by 20 · see citing works
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment
Liu, H., Wu, M., Yuan, K., Sun, M. W., Tang, Y., Zheng, C., Wen, X., & Li, X. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611795 · Cited by 20 · see citing works
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training
Feng, C., Danier, D., Zhang, F., & Bull, D. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00167 · Cited by 20 · see citing works
Q-Boost: On Visual Quality Assessment Ability of Low-Level Multi-Modality Foundation Models
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Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model
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Blind VQA on 360° Video via Progressively Learning From Pixels, Frames, and Video
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GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
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Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos
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2BiVQA: Double Bi-LSTM-based Video Quality Assessment of UGC Videos
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Semantic Information Oriented No-Reference Video Quality Assessment
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Debiased Mapping for Full-Reference Image Quality Assessment
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Subjective Quality Assessment of User-Generated Content Gaming Videos
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Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment
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Deep Neural Networks For Full-Reference And No-Reference Audio-Visual Quality Assessment
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End-to-End Blind Video Quality Assessment Based on Visual and Memory Attention Modeling
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MoA-VR: A Mixture-of-Agents System Toward All-in-One Video Restoration
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A neural network based framework for effective laparoscopic video quality assessment
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Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study
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A Blind Video Quality Assessment Method via Spatiotemporal Pyramid Attention
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Perceptual Quality Assessment of Virtual Reality Videos in the Wild
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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
- 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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