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Journal of Engineering
Volume 2017 (2017), Article ID 4752378, 11 pages
Research Article

Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Correspondence should be addressed to Yong Ding

Received 14 June 2017; Revised 11 August 2017; Accepted 20 August 2017; Published 3 October 2017

Academic Editor: Shang-Hong Lai

Copyright © 2017 Ruizhe Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases.