Edge patterns extracted from natural images and their statistics for reduced-reference image quality assessment

Wenting Shao; Xuanqin Mou

doi:10.1117/12.2008446

4 February 2013 Edge patterns extracted from natural images and their statistics for reduced-reference image quality assessment

Wenting Shao, Xuanqin Mou

Author Affiliations +

Proceedings Volume 8660, Digital Photography IX; 86600G (2013) https://doi.org/10.1117/12.2008446
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Image quality assessment (IQA) aims to predict perceived image quality consistently with the corresponding subjective perceptual quality. Searching for features efficiently representing natural images and investigating their statistics are the fundamentals in the task of IQA models design. In this context, we have proposed previously a novel reduced reference (RR) IQA model in which groups of the named edge patterns are good to represent the local distribution of the zero-crossings both for natural images and their distorted counterpart, and then proposed a RR IQA model. In this paper, we focus on the issue of the interesting edge patterns related to natural images, i.e., what are the edge patterns good at representing ZC distribution of natural images? And how should we do to use them for IQA model design? Along those ideas, we extract 39 groups of edge patterns from 110 natural pictures by a defined curvature rule. Combined with error tolerance, the 39 groups of edge patterns can well represent the ZC distribution of both the reference and distortion images. Based on them, a RR IQA model is built on the statistical analysis of the selected edge patterns. Experimental results show that the proposed model works fairly good compared to its competitor.

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Wenting Shao and Xuanqin Mou "Edge patterns extracted from natural images and their statistics for reduced-reference image quality assessment", Proc. SPIE 8660, Digital Photography IX, 86600G (4 February 2013); https://doi.org/10.1117/12.2008446

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