No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

Huizhen Jia; Quansen Sun; Zexuan Ji; Tonghan Wang; Qiang Chen

doi:10.1117/1.OE.53.11.113110

21 November 2014 No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

Huizhen Jia, Quansen Sun, Zexuan Ji, Tonghan Wang, Qiang Chen

Author Affiliations +

Optical Engineering, Vol. 53, Issue 11, 113110 (November 2014). https://doi.org/10.1117/1.OE.53.11.113110

Abstract

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art “general purpose” NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

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Huizhen Jia, Quansen Sun, Zexuan Ji, Tonghan Wang, and Qiang Chen "No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity," Optical Engineering 53(11), 113110 (21 November 2014). https://doi.org/10.1117/1.OE.53.11.113110

Published: 21 November 2014

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