The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Making a “Completely Blind” Image Quality Analyzer
Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
Study of Subjective and Objective Quality Assessment of Video
Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042111 · Cited by 1,160
Video quality assessment based on structural distortion measurement
Wang, Z., Lu, L., & Bovik, A. C. (2004) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(03)00076-6 · Cited by 1,024
A Survey on Quality of Experience of HTTP Adaptive Streaming
Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2360940 · Cited by 844
Unsupervised feature learning framework for no-reference image quality assessment
Ye, P., Kumar, J., Kang, L., & Doermann, D. S. (2012) · 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/cvpr.2012.6247789 · Cited by 833
Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos
Seshadrinathan, K., & Bovik, A. C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2034992 · Cited by 708 · cited 2×
“Traditional VQAmethods consider structures [47, 48], gradients [21], motion [22, 36], energy [18], saliency [52, 54], or natural video statistics [9, 28, 35, 57].”
BackgroundMethodBlind Prediction of Natural Video Quality
Saad, M. A., Bovik, A. C., & Charrier, C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2299154 · Cited by 471 · cited 5×
“content and may suffer from complex mixed real-world distortions that are temporally heterogeneous. On account of this, current state-of-the-art video quality assessment (VQA) methods (e.g., VBLIINDS [35] and VIIDEO [28]) validated on traditional synthetic VQA databases [30, 38] fail in predicting the quality of in-th…”
MethodInfluential
Research works printed but not linked: 14
- Cho Kyunghyun, 2014, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.
- Dodge Samuel, Understanding how image quality affects deep neural networks
- Fan Lijie, 2018, Boqing Gong, and Junzhou Huang.
- Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778. Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778.
- Hosu Vlad, The Konstanz natural video database (KoNViD-1k)
- Kingma Diederik P, 2014, Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 · arXiv
- Nuutinen Mikko, 2016, CVD2014--A database for evaluating no-reference video quality assessment algorithms. IEEE TIP, 25
- Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. In NIPS-W. Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. In NIPS-W.
- Rimac-Drlje Snjezana, Influence of temporal pooling method on the objective video quality evaluation
- Seufert Michael, A comparison of temporal pooling methods for HTTP adaptive video streaming
- Xu Jingtao, No-reference video quality assessment via feature learning
- Zhang Yu, 2018, Blind Video Quality Assessment with Weakly Supervised Learning and Resampling Strategy. TCSVT
- Zhang Yu, 2019, Objective Video Quality Assessment Combining Transfer Learning With CNN. TNNLS
- Zhu Yun, Blind video quality assessment based on spatio-temporal internal generative mechanism
Cited by
Sorted by citations
Items 201–300 of 391
Study on no-reference video quality assessment method incorporating dual deep learning networks
Li, J., & Li, X. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13383-0 · Cited by 4
Motion measurement and quality variation driven video quality assessment
Hu, Z., & Liu, L. (2022) · Displays · doi:10.1016/j.displa.2022.102289 · Cited by 4
Du, L., Li, J., Li, Z., & Shuo, Y. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13483-x · Cited by 4
Efficient Transformer with Locally Shared Attention for Video Quality Assessment
You, J., & Lin, Y. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9898025 · Cited by 5
Blind Video Quality Assessment via Space-Time Slice Statistics
Zheng, Q., Tu, Z., Hao, Z., Zeng, X., Bovik, A. C., & Fan, Y. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897565 · Cited by 4
No-Reference Video Quality Assessment by Deep Feature Maps Relations
Bakhtiari, A. H., & Mansouri, A. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9960078 · Cited by 4
Spatial-angular Quality-aware Representation Learning for Blind Light Field Image Quality Assessment
Xiang, J., Dang, Y., Chen, P., Liang, R., Huan, R., & Zhang, Z. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611927 · Cited by 4
VStab-QuAD: A New Video-Stabilization Quality Assessment Database
Dakkar, B.-E., Bourki, A., Beghdadi, A., & Cheikh, F. A. (2023) · European Workshop on Visual Information Processing · doi:10.1109/euvip58404.2023.10323042 · Cited by 4
Short-form UGC Video Quality Assessment Based on Multi-Level Video Fusion with Rank-Aware
Xu, H., Yang, M., Zhou, J., & Li, J. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00633 · Cited by 4
Video Quality Analysis: Steps towards Unifying Full and No Reference Cases
Topiwala, P., Dai, W., Pian, J., Biondi, K., & Krovvidi, A. (2022) · Standards · doi:10.3390/standards2030027 · Cited by 4
Hu, B., Wang, W., Li, L., He, L., Lu, W. B., & Gao, X. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3585993 · Cited by 3
Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression
Fernández-Menduiña, S., Xiong, X., Pavéz, E., Ortega, A., Birkbeck, N., & Adsumilli, B. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084420 · Cited by 3
Brain-Inspired Video Quality Assessment via Visual-EEG Feature Alignment
Zhang, C., Hu, S., Li, C., Duan, Y., & Tao, X. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3606204 · Cited by 3
Yan, J., Zhang, J., Chen, J., Chen, P., Liu, X., Tan, Z., & Fang, Y. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02626-w · Cited by 3
A Database for Multi-Modal Short Video Quality Assessment
Zhang, Y., Wang, C., Zhang, S., & Cao, X. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097274 · Cited by 3
Optimized Quality Feature Learning for Video Quality Assessment
Kwong, N.-W., Chan, Y., Tsang, S., & Lun, D. P.-K. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095975 · Cited by 2
ADS-VQA: Adaptive sampling model for video quality assessment
Cheng, S., Li, X., Zeng, Z., & Yan, J. (2024) · Displays · doi:10.1016/j.displa.2024.102792 · Cited by 3
A multi-scale no-reference video quality assessment method based on transformer
Cui, Y., Yu, Z., Feng, Y., Wang, H., & Li, J. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01403-y · Cited by 3
Blind Video Quality Prediction by Uncovering Human Video Perceptual Representation
Liao, L., Xu, K., Wu, H., Chen, C., Sun, W., Yan, Q., Kuo, C. ‐. J., & Lin, W. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3445738 · Cited by 4
Mo, L., Yin, H., Wang, H., Huang, X., Lin, J., Xie, Y., Liu, Y., Sheng, N., & Tang, X. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109819 · Cited by 3
Peng, T., Feng, C., Danier, D., Zhang, F., Vallade, B. Q. A., Mackin, A., & Bull, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91838-4_7 · Cited by 4
Secure Video Quality Assessment Resisting Adversarial Attacks
Zhang, A., Wang, Y., Ran, Y., Tang, W., Guan, Q., & Yang, C. (2025) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2025.3575339 · Cited by 5
Abud, K., Lavrushkin, S., & Vatolin, D. S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9070166 · Cited by 4
VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results
Li, D., Ma, S., Hua, H., Li, W., Wang, J., Zhou, C. W., Guan, F., Li, X., Yu, Z., Lu, Y., et al. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00359 · Cited by 6
Self-citationBompilwar, R., & Koshatwar, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00364 · Cited by 3
Asymmetrically distorted 3D video quality assessment: From the motion variation to perceived quality
Fang, Y., Sui, X., Yan, J., Zuo, Y., Wang, J., & Li, Z. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108031 · Cited by 3
Revisiting the Efficiency of UGC Video Quality Assessment
Wang, Y., Yim, J. G., Birkbeck, N., Ke, J., Talebi, H., Chen, X., Yang, F., & Adsumilli, B. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897401 · Cited by 3
On the Importance of Spatio-Temporal Learning for Video Quality Assessment
Fontanel, D., Higham, D., & Vallade, B. Q. A. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00053 · Cited by 3
DCVQE: A Hierarchical Transformer for Video Quality Assessment
Li, Z., & Yang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26316-3_24 · Cited by 4
Zhang, Y., Yang, M., Huang, Z., He, L., & Wu, Z. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04624-2 · Cited by 3
Feature Maps Correlation-based Video Quality Assessment
Bakhtiari, A. H., & Mansouri, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18068-w · Cited by 3
QPT-V2: Masked Image Modeling Advances Visual Scoring
Xie, Q., Yuan, K., Qu, Y., Wu, M., Sun, M., Zhou, C., & Zhu, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680721 · Cited by 3
Blind Video Quality Assessment at the Edge
Mei, Z., Wang, Y.-C., & Kuo, C. ‐. J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521704 · Cited by 4
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos
Zhu, X., Duan, H., Liu, Y., Zhu, Y., Min, X., Zhai, G., & Callet, P. L. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209505 · Cited by 3
Engagement Prediction of Short Videos with Large Multimodal Models
Sun, W., Cao, L., Cao, Y., Zhang, W., Wen, W., Zhang, K., Chen, Z., Lu, F., Min, X., & Zhai, G. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00360 · Cited by 3
An improved algorithm of video quality assessment by danmaku analysis
Zhang, H., Guo, D., Liu, W., Nie, J., & Li, S. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00858-7 · Cited by 3
Feature‐based no‐reference video quality assessment using Extra Trees
Shahreza, H. O., Amini, A., & Behroozi, H. (2022) · IET Image Processing · doi:10.1049/ipr2.12428 · Cited by 3
Telepresence Video Quality Assessment
Ying, Z., Ghadiyaram, D., & Bovik, A. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_19 · Cited by 3
A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality AssessmentPreprint
Tu, Z., Chen, C.-J., Chen, L., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10651 · Cited by 3
Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality ModelsPreprint
Tu, Z., Chen, C.-J., Chen, L., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00155 · Cited by 3
Video Quality Assessment System Using Deep Optical Flow and Fourier Property
Kang, D., Kim, Y., Kwon, S., Kim, H., Kim, J., & Paik, J. (2023) · IEEE Access · doi:10.1109/access.2023.3335352 · Cited by 2
MVAD: A Multiple Visual Artifact Detector for Video Streaming
Chen, F., Danier, D., Zhang, F., Mackin, A., Collins, A., & Bull, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00311 · Cited by 2
Frame Differences Matter in Quality Assessment of Compressed Videos
Wang, X., Katsenou, A., & Bull, D. (2025) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/dsp65409.2025.11075040 · Cited by 2
Learning Deep ISP for High-Speed Cameras: Achieving DSLR-Quality Imaging Under High Frame Rates
Chen, H., Liu, T., Tu, T., Wei, W., Jin, Y., & Chen, E. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3637771 · Cited by 2
Distortion-Sensitive Masked Autoencoder for Omnidirectional Video Quality Assessment
Hu, Z., Liu, L., Gu, K., Li, L., & Bovik, A. C. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651128 · Cited by 2
Quality Is Not Comfort: Oculomotor Demand Entropy for Short-Form Videos
Kim, T. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3714309 · Cited by 2
Understanding the perceived quality of video predictions
Somraj, N., Kashi, M. S., Arun, S. P., & Soundararajan, R. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116626 · Cited by 2
A Temporal Statistics Model For UGC Video Quality Prediction
Tu, Z., Chen, C.-J., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506669 · Cited by 2
A Random Forest-based No-Reference Quality Metric for UGC Videos
Kumar, K., Mazumdar, P., Jha, K. K., & Lamichhane, K. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31407-0_41 · Cited by 2
A novel blind action quality assessment based on multi-headed GRU network and attention mechanism
Sun, W., Hu, Y., Zhang, B., Chen, X., Hao, C., & Gao, Y. (2023) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1117/12.2685368 · Cited by 2
Kwon, J., Jiao, J., Self, A., Noble, J. A., & Papageorghiou, A. T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-39539-0_12 · Cited by 2
UGC quality assessment: exploring the impact of saliency in deep feature-based quality assessment
Wang, X., Katsenou, A., & Bull, D. (2023) · SPIE Applications of Digital Image Processing · doi:10.1117/12.2676136 · Cited by 2
A Lightweight No-reference Video Quality Assessment Method
Shi, H., Hu, Y., Zhang, Y., & Chen, Z. (2023) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip59821.2023.10402738 · Cited by 2
Tan, X., Zhang, J., Quan, Y., Li, J., Wu, Y., & Bian, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680907 · Cited by 2
End-to-end Prediction of Streaming Video Quality of Experience: Dataset and Approach
Jia, Z., Min, X., & Zhai, G. (2024) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip63160.2024.10849855 · Cited by 2
A Robust Quality Evaluator for Panoramic Videos
Wang, Y., Feng, J., Zhou, Y., Tang, L., Chen, R., Sun, Y., & Ding, J.-C. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888289 · Cited by 2
Who Is a Better Imitator: Subjective and Objective Quality Assessment of Animated Humans
Zhou, Y., Zhang, Z., Jia, J., Jiang, Y., Liu, X., Min, X., & Zhai, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3572000 · Cited by 2
Memory-VQA: Video quality assessment of UGC based on human memory system
Sun, M., Kong, G., Duan, X., & Long, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130852 · Cited by 2
Learning a Blind Quality Evaluator for UGC Videos in Perceptually Relevant Domains
Li, B., Zhang, W., Tian, M., Jiang, J. C., Zhai, G., & Wang, X. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859999 · Cited by 2
Blind consumer video quality assessment with spatial-temporal perception and fusion
Niu, Y., Zheng, Y., Wang, Z., Zhong, M., & Zhao, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16242-8 · Cited by 2
Quality assessment of enhanced videos guided by aesthetics and technical quality attributes
Agarla, M., Celona, L., Rota, C., & Schettini, R. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00156 · Cited by 2
Quality of Experience Assessment for Free-viewpoint Video
Jia, R., Zhang, Y., Xu, J., Zhang, W., Song, L., Li, L., & Feng, Y. (2023) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb58369.2023.10211612 · Cited by 2
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
Falahati, A., Safavi, M. K., Elahi, A., Pakdaman, F., & Gabbouj, M. (2024) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/mvip62238.2024.10491154 · Cited by 2
Subjective Study of Perceptual Quality for Micro-Video Applications
Hu, Y., Zhang, Y., Liu, Z., Chen, Z., & Liu, S. (2020) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr49039.2020.00054 · Cited by 2
A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms
Kwan, C., & Budavari, B. (2021) · Electronics · doi:10.3390/electronics10182277 · Cited by 2
StarVQA: Space-Time Attention for Video Quality AssessmentPreprint
Xing, F., Wang, Y., Wang, H., Li, L., & Zhu, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.09635 · Cited by 2
An End-to-End Fast No-Reference Video Quality Predictor with Spatiotemporal Feature Fusion
Vishwakarma, A. K., & Bhurchandi, K. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31417-9_48 · Cited by 1
STAN: Spatio-Temporal Alignment Network for No-Reference Video Quality Assessment
Yang, Z., Dang, Y., Xiang, J., & Chen, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44213-1_14 · Cited by 1
MT-VQA: A Multi-task Approach for Quality Assessment of Short-form Videos
Wen, S., Qiao, M., Jiang, L. W., Xu, M., Deng, X., & Li, S. (2024) · Workshop on Quality of Experience in Visual Multimedia Applications (QoEVMA) · doi:10.1145/3689093.3689181 · Cited by 1
ACORN+: Adaptive Compression-Reconstruction for Device-Cloud Collaboration Video Services
Lei, J., Yang, P., Kong, L., Ma, Y., Lin, D., Chen, G., & Zhao, E. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3703000 · Cited by 1
Quantizing Neural Networks with Knowledge Distillation for Efficient Video Quality Assessment
Yu, J. Y., & Li, Y. (2024) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip63160.2024.10849844 · Cited by 1
Exploring Simple Siamese Network for High-Resolution Video Quality Assessment
Shen, G., Yan, Z., Jin, X., Wu, L., Chen, J., Cho, I., & Hahm, C.-H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889618 · Cited by 1
A study on the user viewing experience of implanted advertisement videos based on visual saliency
Lu, F., Lian, Y., Sun, W., Min, X., & Zhai, G. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128499 · Cited by 1
Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos
Zheng, Q., Chen, L., He, C., Birkbeck, N., Wang, Y., Adsumilli, B., Bovik, A. C., Fan, Y., & Tu, Z. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3592543 · Cited by 1
DIVA-VQA: Detecting Inter-Frame Variations in UGC Video Quality
Wang, X., Katsenou, A., & Bull, D. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084568 · Cited by 1
LMVQ: Label-Free Metric-Learning for General AI-Generated Video Quality Assessment
Zhang, Z., Li, X., Sun, W., Zhang, Z., Liu, X., Min, X., & Zhai, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3618655 · Cited by 1
RTSIQA: A database and method for real-world traffic scenes image quality assessment
Lu, F., Ni, H., Huang, Y., Guo, N., Zhang, K., Sun, W., & Min, X. (2025) · Displays · doi:10.1016/j.displa.2025.103299 · Cited by 1
MSTVQA: A multi-path dynamic perception method for video quality assessment
Qi, J., Wang, Y., Gao, J., Wu, Y., & Liu, P. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105891 · Cited by 1
Compressed Video Quality Assessment With Fine-Grained Artifact Perception and Evaluation
Lin, L., Zhang, Y., Chen, B., Yang, J., & Zhao, T. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651043 · Cited by 1
AIGVQA: A Unified Framework for Multi-Dimensional Quality Assessment of AI-Generated Video
Wang, J., Wang, J., Zhu, X., Duan, H., Zhai, G., & Min, X. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00358 · Cited by 1
3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression
Xing, Y., Gordon, W., Yang, Q., Yang, K., Wang, J., & Xu, Y. (2025) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip67698.2025.11396862 · Cited by 1
Subjective and Objective QoE Assessment for Real-Time Streaming Interactive Digital Human
Zhou, Y., Wan, J., Wen, F., Zhang, Z., Liu, X., Zhou, Y., Cao, J., Wang, Y., Min, X., & Zhai, G. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3820052 · Cited by 1
A Dual-Task Deep Network for Video Distortion EEG Decoding and Quality Assessment
Chen, J., Xu, G., Xu, T., Zhou, T., & Hu, H. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3710435 · Cited by 1
GameSR: Real-Time Superresolution for Interactive Gaming
Rehman, H. U., Shirmohammadi, S., Amer, I., & Hefeeda, M. M. (2026) · IEEE Transactions on Games · doi:10.1109/tg.2026.3710843 · Cited by 1
DHQA-4D: A large-scale dataset and LMM-based metric for dynamic 4D digital human quality assessment
Li, Y., Wu, S., Zhu, Y., Duan, H., Zhang, Z., Sun, W., Min, X., & Zhai, G. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114567 · Cited by 1
On quality control of user-generated-content (UGC) compression
Li, Y., Zhang, X., Wang, S., Ma, S., & Kuo, C. ‐. J. (2021) · SPIE Applications of Digital Image Processing · doi:10.1117/12.2595177 · Cited by 1
RAVA: Region-Based Average Video Quality Assessment
Wu, X., Cheng, I., Zhou, Z., & Basu, A. (2021) · Sensors · doi:10.3390/s21165489 · Cited by 1
Blindly Predict Image and Video Quality in the Wild
Tang, J., Fang, Y., Dong, Y., Xie, R., Gu, X., Zhai, G., & Song, L. (2021) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3469877.3490588 · Cited by 1
Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff
Pang, Y., Zhao, S., Wang, H., Zhan, G., Li, J., & Zhang, L. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00472 · Cited by 1
Blind Video Stabilization Quality Assessment based on convolutional LSTM
Yagoubi, M. R., Amirshahi, S. A., Moan, S. L., Beghdadi, A., & Rodner, E. (2023) · European Workshop on Visual Information Processing · doi:10.1109/euvip58404.2023.10323043 · Cited by 1
Mi, Y., Shu, Y., Xu, H., Liu, S., & Jiang, F. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8540-1_28 · Cited by 1
STVP: A Spatiotemporal Visual Perception Method for User-generated Content Video Quality Assessment
Ai, D., Lu, M., Wang, J., & Liu, Y. (2023) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip59821.2023.10402648 · Cited by 1
Spatiotemporal feature learning for no-reference gaming content video quality assessment
Kwong, N.-W., Chan, Y., Tsang, S., Huang, Z., & Lam, K. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104118 · Cited by 1
Blind Quality Assessment of Panoramic Images Based on Multiple Viewport Sequences
Liu, X., Yan, J., Wan, Z., Fang, Y., & Liu, H. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558321 · Cited by 1
Priorformer: A UGC-VQA Method With Content and Distortion Priors
Pei, Y., Huang, S., Lu, Y., Li, X., & Chen, Z. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647869 · Cited by 1
DVL2024: A Larger Ultra-High-Definition Video Dataset for Perceptual Quality Study
He, J., Yuan, J., & Xing, F. (2025) · IEEE Access · doi:10.1109/access.2025.3541021 · Cited by 1
ADCMT: An Augmentation-Free Dynamic Contrastive Multi-Task Transformer for UGC-VQA
Li, H., Zhang, K., Li, J., Gao, X., & Shi, G. (2025) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2025.3565888 · Cited by 1
Kwong, N.-W., Chan, Y., Tsang, S., Huang, Z., & Lam, K. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3599071 · Cited by 1
Reproducibility Companion Paper
Korhonen, J., Su, Y., You, J., Hicks, S. A., & Midoglu, C. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3477933 · Cited by 1
User-Generated Content (UGC)/In-The-Wild Video Content Recognition
Leszczuk, M., Janowski, L., Nawała, J., & Grega, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21967-2_29 · Cited by 1
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
