Cited by 641Image and Video Quality AssessmentImage Retrieval and Classification TechniquesDigital Media Forensic Detection

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Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

Xue, Wufeng, Mou, Xuanqin, Zhang, Lei, Bovik, Alan C., Feng, Xiangchu·2014·IEEE Transactions on Image Processing

DOI 10.1109/tip.2014.2355716

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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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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
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SURF: Speeded Up Robust Features
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“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.”
Method
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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
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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
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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

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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)
ORCID: Xue, Wufeng
Mou, Xuanqin
Bovik, Alan C.
Feng, Xiangchu
Venue: IEEE Transactions on Image Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2014-11
Volume: 23
Issue: 11
Pages: 4850-4862
ISSN: 1057-7149 (ISSN-L)
1941-0042
DOI: 10.1109/tip.2014.2355716
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
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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

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Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features

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