The DOI matches its Crossref record; 434 of the 434 references it cites were found in a registry or an index.
Perceptual image quality assessment: a survey
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,667
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Proceedings of IEEE International Conference on Computer Vision
(1995) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466933 · Cited by 12,860
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
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
Multiscale 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
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
FSIM: A Feature Similarity Index for Image Quality Assessment
Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2109730 · Cited by 5,203
Cited by
AdaFace: Quality Adaptive Margin for Face Recognition
Kim, M., Jain, A. K., & Liu, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01819 · Cited by 584
Perceptual video quality assessment: a survey
Min, X., Duan, H., Sun, W., Zhu, Y., & Zhai, G. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4133-3 · Cited by 218
Self-citationDeepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 134
“For more about PQA metrics for different types of multimedia signals, we refer readers to some relevant surveys (Akhtar and Falk, 2017; Pal and Triyason, 2018; Zhai and Min, 2020).”
BackgroundDeep 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
Self-citationImage Quality Assessment: From Mean Opinion Score to Opinion Score Distribution
Gao, Y., Min, X., Zhu, Y., Li, J., Zhang, X.-P., & Zhai, G. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3547872 · Cited by 58
Self-citation
Metadata
- Authors
- Zhai, G., & Min, X.
- Venue
- Science China Information Sciences
- Published
- 2020-04-26
- Volume
- 63
- Issue
- 11
- ISSN
- 1869-1919 (ISSN-L)
- 1674-733X
- OpenAlex
- W3022710784
- Cited by
- 655
- References
- 434
- FWCI
- 33.20
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Perceptual image quality assessment: a survey. https://app.cochat.ai/papers/10.1007/s11432-019-2757-1
- The paper
- Zhai, G., & Min, X. (2020). Perceptual image quality assessment: a survey. Science China Information Sciences. https://doi.org/10.1007/s11432-019-2757-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
