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Journal of Electronic Imaging

Blind image quality assessment using statistical independence in the divisive normalization transform domain
Author(s): Ying Chu; Xuanqin Mou; Hong Fu; Zhen Ji
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Paper 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.

Paper Details

Date Published: 23 November 2015
PDF: 20 pages
J. Electron. Imag. 24(6) 063008 doi: 10.1117/1.JEI.24.6.063008
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Ying Chu, Shenzhen Univ. (China)
Xi’an Jiaotong Univ. (China)
Xuanqin Mou, Xi'an Jiaotong Univ. (China)
Beijing Ctr. for Mathematics and Information Interdisciplinary Sciences (China)
Hong Fu, Chu Hai College of Higher Education (Hong Kong)
Zhen Ji, Shenzhen Univ. (China)

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