A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment
Abstract
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pooling methods on noreference (blind) VQA. Here we conduct a large-scale comparative evaluation to assess the capabilities and limitations of multiple temporal pooling strategies on blind VQA of usergenerated videos. The study yields insights and general guidance regarding the application and selection of temporal pooling models. In addition, we also propose an ensemble pooling model built on top of high-performing temporal pooling models. Our experimental results demonstrate the relative efficacies of the evaluated temporal pooling models, using several popular VQA algorithms evaluated on two recent largescale natural video quality databases. Conclusively, we also provide an empirical recipe for applying temporal pooling of frame-based quality predictions.
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Temporal pooling is one of the most important, unresolved problems for video quality prediction.
“Temporal pooling is one of the most important, unresolved problems for video quality prediction [42], [60], [76], [82], [83].”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [76]
The authors observed slightly different results using BVQA testbeds as compared to what they observed on BIQA [76].
“Here we observed yet slightly different results using BVQA testbeds as compared to what we observed on BIQA [76].”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [76]
In previous work [76], the authors studied the efficacy of various pooling methods using scores predicted by BIQA models.
“In our previous work [76], we have studied the efficacy of various pooling methods using scores predicted by BIQA models.”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [76]
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Self-citationNo-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 · see citing works
Self-citationThe serial position effect of free recall.
Murdock, B. B. (1962) · Journal of Experimental Psychology · doi:10.1037/h0045106 · Cited by 1,717 · cited 3× · see citing works
“Recency Effect: The recency effect is another well-established behavioral and memory effect, whereby, in this context, video quality is very strongly influenced by a viewer’s most recently percieved visual impression [24].”
BackgroundInfluentialBlind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2355716 · Cited by 641 · see citing works
Self-citationBlind 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 1× · see citing works
“Simple temporal average pooling is a widely used scheme to augment BVQA models [5,7,10].”
MethodSelf-citationQuality Assessment of In-the-Wild Videos
Li, D., Jiang, T., & Jiang, M. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3351028 · Cited by 387 · see citing works
A Completely Blind Video Integrity Oracle
Mittal, A., Saad, M. A., & Bovik, A. C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2502725 · Cited by 357 · cited 1× · see citing works
“Simple temporal average pooling is a widely used scheme to augment BVQA models [5,7,10].”
MethodSelf-citationKim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., & Bovik, A. C. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2736018 · Cited by 283 · see citing works
Self-citationBBAND INDEX: A NO-REFERENCE BANDING ARTIFACT PREDICTOR
Tu, Z., Lin, J., Wang, Y., Adsumilli, B., & Bovik, A. C. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9053634 · Cited by 46 · see citing works
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UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3072221 · Cited by 363 · see citing works
“In our previous work [76], we have studied the efficacy”
BackgroundMethodResultInfluentialSelf-citationTu, Z., Lin, J., Wang, Y., Adsumilli, B., & Bovik, A. C. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3024985 · Cited by 31 · see citing works
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- Authors
- Tu, Z., Chen, C.-J., Chen, L.-H., Birkbeck, N., Adsumilli, B., & Bovik, A. C.
- Affiliations
- The University of Texas at Austin (US)
- Google (United States) (US)
- Venue
- 2020 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2020-10
- Pages
- 141-145
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W3090101394
- Cited by
- 71
- References
- 9
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 11 · 2023: 10 · 2022: 22 · 2021: 18 · 2020: 4
- FWCI
- 4.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- temporal pooling, blind video quality assessment, user-generated video, objective video quality metrics
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