The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Blind Image Quality Assessment Using a General Regression Neural Network
Abstract
We develop a no-reference image quality assessment (QA) algorithm that deploys a general regression neural network (GRNN). The new algorithm is trained on and successfully assesses image quality, relative to human subjectivity, across a range of distortion types. The features deployed for QA include the mean value of phase congruency image, the entropy of phase congruency image, the entropy of the distorted image, and the gradient of the distorted image. Image quality estimation is accomplished by approximating the functional relationship between these features and subjective mean opinion scores using a GRNN. Our experimental results show that the new method accords closely with human subjective judgment.
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
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 1×
“Similarity index (MS-SSIM) [ 2 ] and its variants [9], the visual information fidelity index [10], and the visual several prominent FR objective metrics have been demonstrated to have much higher correlation”
MethodSelf-citationMean squared error: Love it or leave it? A new look at Signal Fidelity Measures
Wang, Z., & Bovik, A. C. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.930649 · Cited by 3,175
Self-citationImage quality measures and their performance
Eskicioglu, A. M., & Fisher, P. S. (1995) · IEEE Transactions on Communications · doi:10.1109/26.477498 · Cited by 2,117
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
Chandler, D. M., & Hemami, S. S. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901820 · Cited by 1,147
No-reference quality assessment using natural scene statistics: JPEG2000
Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.854492 · Cited by 575
Self-citationBlind measurement of blocking artifacts in images
Wang, Z., Bovik, A. C., & Evan, B. L. (2002) · Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101) · doi:10.1109/icip.2000.899622 · Cited by 413
Self-citationBlind image quality assessment through anisotropy
Gabarda, S., & Cristóbal, G. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.000b42 · Cited by 301
Wang, Z., Wu, G., Sheikh, H. R., Simoncelli, E. P., Yang, E.-H., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.864165 · Cited by 293
Self-citation
Cited by
A Feature-Enriched Completely Blind Image Quality Evaluator
Zhang, L., Zhang, L., & Bovik, A. C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2426416 · Cited by 1,311
Self-citationUnsupervised feature learning framework for no-reference image quality assessment
Ye, P., Kumar, J., Kang, L., & Doermann, D. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247789 · Cited by 785
Fully Deep Blind Image Quality Predictor
Kim, J., & Lee, S. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2639328 · Cited by 459
Deep CNN-Based Blind Image Quality Predictor
Kim, J., Nguyen, A.-D., & Lee, S. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2829819 · Cited by 331
Metadata
- Authors
- Li, C., Bovik, A. C., & Wu, X.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2011-05
- Volume
- 22
- Issue
- 5
- Pages
- 793-799
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2140094223
- Cited by
- 290
- References
- 9
- FWCI
- 19.67
- 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: Blind Image Quality Assessment Using a General Regression Neural Network. https://app.cochat.ai/papers/10.1109/tnn.2011.2120620
- The paper
- Li, C., Bovik, A. C., & Wu, X. (2011). Blind Image Quality Assessment Using a General Regression Neural Network. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2011.2120620
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.
