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Blind image quality assessment using statistical independence in the divisive normalization transform domain

[+] Author Affiliations
Ying Chu

Shenzhen University, College of Computer Science and Software Engineering, Shenzhen Key Laboratory of Embedded Systems Design, No. 3688, Nanhai Avenue, Shenzhen 518060, China

Xi’an Jiaotong University, School of Electronic and Information Engineering, Institute of Image Processing and Pattern Recognition, No. 28, Xianning West Road, Xi’an 710049, China

Xuanqin Mou

Xi’an Jiaotong University, School of Electronic and Information Engineering, Institute of Image Processing and Pattern Recognition, No. 28, Xianning West Road, Xi’an 710049, China

Beijing Center for Mathematics and Information Interdisciplinary Sciences, No. 105, West Third Ring Road North, Beijing 100048, China

Hong Fu

Chu Hai College of Higher Education, Department of Computer Science, Yi Lok Street, Riviera Gardens, Tsuen Wan, New Territories 999077, Hong Kong

Zhen Ji

Shenzhen University, College of Computer Science and Software Engineering, Shenzhen Key Laboratory of Embedded Systems Design, No. 3688, Nanhai Avenue, Shenzhen 518060, China

J. Electron. Imaging. 24(6), 063008 (Nov 23, 2015). doi:10.1117/1.JEI.24.6.063008
History: Received October 23, 2014; Accepted October 20, 2015
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Abstract.  We present a general purpose blind image quality assessment (IQA) method using the statistical independence hidden in the joint distributions of divisive normalization transform (DNT) representations for natural images. The DNT simulates the redundancy reduction process of the human visual system and has good statistical independence for natural undistorted images; meanwhile, this statistical independence changes as the images suffer from distortion. Inspired by this, we investigate the changes in statistical independence between neighboring DNT outputs across the space and scale for distorted images and propose an independence uncertainty index as a blind IQA (BIQA) feature to measure the image changes. The extracted features are then fed into a regression model to predict the image quality. The proposed BIQA metric is called statistical independence (STAIND). We evaluated STAIND on five public databases: LIVE, CSIQ, TID2013, IRCCyN/IVC Art IQA, and intentionally blurred background images. The performances are relatively high for both single- and cross-database experiments. When compared with the state-of-the-art BIQA algorithms, as well as representative full-reference IQA metrics, such as SSIM, STAIND shows fairly good performance in terms of quality prediction accuracy, stability, robustness, and computational costs.

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Citation

Ying Chu ; Xuanqin Mou ; Hong Fu and Zhen Ji
"Blind image quality assessment using statistical independence in the divisive normalization transform domain", J. Electron. Imaging. 24(6), 063008 (Nov 23, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063008


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