The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
Abstract
Blind image quality assessment (BIQA) aims to evaluate the perceptual quality of a distorted image without information regarding its reference image. Existing BIQA models usually predict the image quality by analyzing the image statistics in some transformed domain, e.g., in the discrete cosine transform domain or wavelet domain. Though great progress has been made in recent years, BIQA is still a very challenging task due to the lack of a reference image. Considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we propose a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian (LOG) response. We employ an adaptive procedure to jointly normalize the GM and LOG features, and show that the joint statistics of normalized GM and LOG features have desirable properties for the BIQA task. The proposed model is extensively evaluated on three large-scale benchmark databases, and shown to deliver highly competitive performance with state-of-the-art BIQA models, as well as with some well-known full reference image quality assessment models.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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-citationDistinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,463
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1×
“including, for example, the Harris corner detection [47], the SIFT/SUFR [48], [49] operators for object matching, and the HOG [50] features used for human detection, to name a few.”
MethodA tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,723
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Robust Face Recognition via Sparse Representation
Wright, J. N., Yang, A., Ganesh, A., Sastry, S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,398
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
Self-citation
Research works printed but not linked: 3
- 10.1088/0954-898X/5/4/006 · DOI
- bouman, 1995, Markov random fields and stochastic image models, Proc ICIP, 1
- 2003, VQEG Final Report From the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment
Cited by
Sorted by citations
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-citationBlind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
Zhang, W., Ma, K., Yan, J., Deng, D., & Wang, Z. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2886771 · Cited by 1,027
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., & Zhang, Y. (2020) · 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/cvpr42600.2020.00372 · Cited by 963
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models
Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2631888 · Cited by 789
Perceptual image quality assessment: a survey
Zhai, G., & Min, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2757-1 · Cited by 686
Metadata
- Authors
- Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X.
- Affiliations
- Xi'an Jiaotong University (CN)
- The University of Texas at Austin (US)
- Xidian University (CN)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2014-11
- Volume
- 23
- Issue
- 11
- Pages
- 4850-4862
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1977246677
- Cited by
- 641
- References
- 51
- Citations by year
- 2026: 18 · 2025: 28 · 2024: 68 · 2023: 72 · 2022: 85 · 2021: 70 · 2020: 64 · 2019: 64 · 2018: 63 · 2017: 46 · 2016: 41 · 2015: 22
- FWCI
- 32.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61172163, 90920003, 61271294)
- Research Grants Council, Hong Kong (PolyU-5315/12E)
- National Science Foundation (IIS-0917175, IIS-1116656)
- 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
- no-reference image quality assessment, gradient magnitude, Laplacian of Gaussian, feature normalization, perceptual image quality, image distortion
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
