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Correction published on 26 May 2017, see Algorithms 2017, 10(2), 60.

Open AccessArticle
Algorithms 2016, 9(4), 87; doi:10.3390/a9040087

A No Reference Image Quality Assessment Metric Based on Visual Perception

College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
This paper is an extended version of our paper published in the International Symposium on Computer, Consumer and Control, Xi’an, China, 4–6 July 2016.
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Author to whom correspondence should be addressed.
Academic Editor: Hsiung-Cheng Lin
Received: 30 August 2016 / Revised: 8 December 2016 / Accepted: 12 December 2016 / Published: 16 December 2016
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Abstract

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality. View Full-Text
Keywords: image quality assessment; no reference; visual perception; support vector regression; region of interest; generalized Gauss distribution (GGD) image quality assessment; no reference; visual perception; support vector regression; region of interest; generalized Gauss distribution (GGD)
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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. (CC BY 4.0).

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Fu, Y.; Wang, S. A No Reference Image Quality Assessment Metric Based on Visual Perception. Algorithms 2016, 9, 87.

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