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Open Access

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Perceptual Gradient Similarity Deviation for Full Reference Image Quality Assessment

Manyu Jin1, Tao Wang1, Zexuan Ji1,*, Xiaobo Shen2
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
School of Computer Science and Engineering, Nanyang Technology University, 639798, Singapore.
* Corresponding Author: Zexuan Ji. Email: .

Computers, Materials & Continua 2018, 56(3), 501-515. https://doi.org/ 10.3970/cmc.2018.02371

Abstract

Perceptual image quality assessment (IQA) is one of the most indispensable yet challenging problems in image processing and computer vision. It is quite necessary to develop automatic and efficient approaches that can accurately predict perceptual image quality consistently with human subjective evaluation. To further improve the prediction accuracy for the distortion of color images, in this paper, we propose a novel effective and efficient IQA model, called perceptual gradient similarity deviation (PGSD). Based on the gradient magnitude similarity, we proposed a gradient direction selection method to automatically determine the pixel-wise perceptual gradient. The luminance and chrominance channels are both took into account to characterize the quality degradation caused by intensity and color distortions. Finally, a multi-scale strategy is utilized and pooled with different weights to incorporate image details at different resolutions. Experimental results on LIVE, CSIQ and TID2013 databases demonstrate the superior performances of the proposed algorithm

Keywords

Image quality assessment, full reference, perceptual gradient similarity, multi-scale, standard deviation pooling

Cite This Article

M. . Jin, T. . Wang, Z. . Ji and X. . Shen, "Perceptual gradient similarity deviation for full reference image quality assessment," Computers, Materials & Continua, vol. 56, no.3, pp. 501–515, 2018.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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