Blind Image Quality Assessment Using Statistical Structural and Luminance Features

Blind image quality assessment (BIQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce a novel BIQA metric by structural and luminance information, based on the...

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Bibliographic Details
Published inIEEE transactions on multimedia Vol. 18; no. 12; pp. 2457 - 2469
Main Authors Li, Qiaohong, Lin, Weisi, Xu, Jingtao, Fang, Yuming
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Blind image quality assessment (BIQA)
Data mining
Feature extraction
human visual system (HVS)
Image quality
Luminance
Measurement
no-reference (NR)
Nonlinear distortion
Quality assessment
structural distortion
Support vector machines
Visualization
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Summary:Blind image quality assessment (BIQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce a novel BIQA metric by structural and luminance information, based on the characteristics of human visual perception for distorted image. We extract the perceptual structural features of distorted image by the local binary pattern distribution. Besides, the distribution of normalized luminance magnitudes is extracted to represent the luminance changes in distorted image. After extracting the features for structures and luminance, support vector regression is adopted to model the complex nonlinear relationship from feature space to quality measure. The proposed BIQA model is called no-reference quality assessment using statistical structural and luminance features (NRSL). Extensive experiments conducted on four synthetically distorted image databases and three naturally distorted image databases have demonstrated that the proposed NRSL metric compares favorably with the relevant state-of-the-art BIQA models in terms of high correlation with human subjective ratings. The MATLAB source code and validation results of NRSL are publicly online at http://www.ntu.edu.sg/home/wslin/Publications.htm.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2016.2601028