Study of Subjective and Objective Quality Assessment of Video
Abstract
We present the results of a recent large-scale subjective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to assess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online.
Integrity signals
Registry
Cites
Statistics
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?
ViS3 has been shown to perform well in predicting the quality of videos from the LIVE, IVPL, and CSIQ databases.
“The algorithm has been shown to perform well in predicting quality of videos from the LIVE database,24 the IVPL database,62 and the CSIQ database.63”
From ViS3: an algorithm for video quality assessment via analysis of spatial and spatiotemporal slices (2014) · cites this paper as [24]
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-citationMultiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253 · cited 2× · see citing works
“of two of the algorithms (MS-SSIM [32] and the VQM from”
MethodSelf-citationA new standardized method for objectively measuring video quality
Pinson, M. H., & Wolf, S. (2004) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2004.834028 · Cited by 1,182 · cited 3× · see citing works
“NTIA [38]) affirm both of these algorithms as extremely prac-”
MethodInfluentialMotion 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 5× · see citing works
“However, this difference in performance is not statistically significant and further, MOVIE performs better than Temporal MOVIE on the wireless and IP videos in terms of LCC and on the VQEG database [43].”
BackgroundMethodResultInfluentialSelf-citation
Flagged first, then by citations
Cited by
Sorted by citations
Blind 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 · see citing works
Self-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
Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing
Soundararajan, R., & Bovik, A. C. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2214933 · Cited by 368 · see citing works
Self-citationA 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 · see citing works
Self-citationUGC-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
“Figure 6 displays the histogram of MOS distributions for the three UGC-VQA databases.”
MethodInfluentialSelf-citationReduced- and No-Reference Image Quality Assessment
Wang, Z., & Bovik, A. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942471 · Cited by 280 · see citing works
Self-citationViS3: an algorithm for video quality assessment via analysis of spatial and spatiotemporal slices
Vu, P. V., & Chandler, D. M. (2014) · Journal of Electronic Imaging · doi:10.1117/1.jei.23.1.013016 · Cited by 185 · see citing works
“; otherwise ; (24) σ̃ y ðbÞ 1⁄4 8< : 0; if μ y ðbÞ < p σ y ðbÞ × ffiffiffiffiffiffiffiffiffiffiffiffiffiffi μ y ðbÞ pþμ y ðbÞ q”
A spatiotemporal most-apparent-distortion model for video quality assessment
Vu, P. V., Vu, C. T., & Chandler, D. M. (2011) · 2011 18th IEEE International Conference on Image Processing · doi:10.1109/icip.2011.6116171 · Cited by 180 · see citing works
Blind omnidirectional image quality assessment with viewport oriented graph convolutional networks
Xu, J., Zhou, W., & Chen, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3015186 · Cited by 166 · see citing works
SpEED-QA: Spatial Efficient Entropic Differencing for Image and Video Quality
Bampis, C. G., Gupta, P., Soundararajan, R., & Bovik, A. C. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2726542 · Cited by 150 · see citing works
Self-citationVideo Quality Pooling Adaptive to Perceptual Distortion Severity
Park, J., Seshadrinathan, K., Lee, S., & Bovik, A. C. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2219551 · Cited by 119 · see citing works
Self-citationRecurrent and Dynamic Models for Predicting Streaming Video Quality of Experience
Bampis, C. G., Li, Z., Katsavounidis, I., & Bovik, A. C. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2815842 · Cited by 84 · see citing works
Self-citationDeep neural network for blind visual quality assessment of 4K content
Lu, W., Sun, W., Min, X., Zhu, W., Zhou, Q., He, J., Wang, Q., Zhang, Z., Wang, T., & Zhai, G. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2022.3221689 · Cited by 60 · see citing works
Survey on QoE Assessment Approach for Network Service
Yang, M., Wang, S., Calheiros, R. N., & Yang, F. (2018) · IEEE Access · doi:10.1109/access.2018.2867253 · Cited by 54 · see citing works
Quality assessment in the era of large models: A survey
Zhang, Z., Zhou, Y., Li, C., Zhao, B., Liu, X., & Zhai, G. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3722559 · Cited by 23 · see citing works
- Authors
- Seshadrinathan, K., Soundararajan, R., Bovik, A. C., & Cormack, L. K.
- Affiliations
- Intel (United States) (US)
- The University of Texas at Austin (US)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2010-06
- Volume
- 19
- Issue
- 6
- Pages
- 1427-1441
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2117535912
- Cited by
- 1,160
- References
- 4
- Citations by year
- 2026: 18 · 2025: 41 · 2024: 52 · 2023: 50 · 2022: 69 · 2021: 95 · 2020: 70 · 2019: 63 · 2018: 94 · 2017: 86 · 2016: 78 · 2015: 102 · 2014: 108 · 2013: 84 · 2012: 79
- FWCI
- 80.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (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 › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- video quality assessment, subjective quality assessment, full-reference quality metrics, video distortion, benchmark database, algorithm performance evaluation
- MeSH terms
- Algorithms, Humans, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Video Recording, Visual Perception, Reproducibility of Results, Observer Variation
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
