Imaging Components, Systems, and Processing

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

[+] Author Affiliations
Huizhen Jia

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Quansen Sun

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Zexuan Ji

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Tonghan Wang

Southeast University, School of Computer Science and Engineering, Laboratory of Image Science and Technology, Nanjing 210096, China

Qiang Chen

Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing 210094, China

Opt. Eng. 53(11), 113110 (Nov 21, 2014). doi:10.1117/1.OE.53.11.113110
History: Received May 14, 2014; Accepted October 22, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

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", Opt. Eng. 53(11), 113110 (Nov 21, 2014). ; http://dx.doi.org/10.1117/1.OE.53.11.113110


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