21 November 2014 No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity
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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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Huizhen Jia, Huizhen Jia, Quansen Sun, Quansen Sun, Zexuan Ji, Zexuan Ji, Tonghan Wang, Tonghan Wang, Qiang Chen, 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 . Submission:
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