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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.
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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 ›
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