Reduced reference image quality assessment via sub-image similarity based redundancy measurement

Xuanqin Mou; Wufeng Xue; Lei Zhang

doi:10.1117/12.908161

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20 February 2012 Reduced reference image quality assessment via sub-image similarity based redundancy measurement

Xuanqin Mou, Wufeng Xue, Lei Zhang

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Proceedings Volume 8291, Human Vision and Electronic Imaging XVII; 82911S (2012) https://doi.org/10.1117/12.908161
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge) between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.

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Xuanqin Mou, Wufeng Xue, and Lei Zhang "Reduced reference image quality assessment via sub-image similarity based redundancy measurement", Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82911S (20 February 2012); https://doi.org/10.1117/12.908161

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