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 <i>Sub-image Similarity (SIS)</i>, 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 <i>(Non-shift Edge)</i> 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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