No-reference Image Quality Assessment Based on Differential Excitation

被引:0
作者
Chen Y. [1 ]
Wu M.-M. [1 ]
Fang H. [1 ]
Liu H.-L. [2 ]
机构
[1] Key Laboratory of Industrial Internet of Thing and Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing
[2] School of Telecommunications and Information, Chongqing University of Posts and Telecommunications, Chongqing
来源
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 08期
基金
中国国家自然科学基金;
关键词
Differential incentives; Gradient mapping; Image quality assessment; Weber's law;
D O I
10.16383/j.aas.c180088
中图分类号
学科分类号
摘要
In order to estimate the degradation of the image distortion level and consider the correlation among pixels, a no-reference image quality assessment algorithm based on differential excitation is proposed in this article. According to the Weber's law, the differential excitation map was obtained and the gradient map of differential excitation was obtained by anisotropy. Then, the differential quantization map was obtained by quantifying differential excitation, and weighted fusion with differential excitation map and gradient map is carried out respectively. Finally, the objective evaluation value of image quality is obtained by support vector regression (SVR) prediction using the acquired features. In LIVE and MLIVE and MDID2013 and MDID2016 databases, the experiment shows that the algorithm is highly robust and low complexity, which can accurately reflect the human image quality of visual perception. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码:1727 / 1737
页数:10
相关论文
共 27 条
[1]  
Wang Chao-Yun, Jiang Gang-Yi, Yu Mei, Chen Fen, Manifold feature similarity based perceptual image quality assessment, Acta Automatica Sinica, 42, 7, pp. 1113-1124, (2016)
[2]  
Nan Dong, Bi Du-Yan, Ma Shi-Ping, Fan Zun-Lin, He Lin-Yuan, A quality assessment method with classified-learning for Dehazed images, Acta Automatica Sinica, 42, 2, pp. 270-278, (2016)
[3]  
Wang Zhi-Ming, Review of no-reference image quality assessment, Acta Automatica Sinica, 41, 6, pp. 1062-1079, (2015)
[4]  
Chen Yong, Shuai Feng, Fan Qiang, A no-reference image quality assessment based on distribution characteristics of natural statistics, Journal of Electronics and Information Technology, 38, 7, pp. 1645-1653, (2016)
[5]  
Moorthy A K, Bovik A C., Blind image quality assessment: From natural scene statistics to perceptual quality, IEEE Transactions on Image Processing, 20, 12, pp. 3350-3364, (2011)
[6]  
Mittal A, Moorthy A K, Bovik A C., No-reference image quality assessment in the spatial domain, IEEE Transactions on Image Processing, 21, 12, pp. 4695-4708, (2012)
[7]  
Mittal A, Soundararajan R, Bovik A C., Making a "completely blind" image quality analyzer, IEEE Signal Processing Letters, 20, 3, pp. 209-212, (2013)
[8]  
Zhang L, Zhang L, Bovik A C., A feature-enriched completely blind image quality evaluator, IEEE Transactions on Image Processing, 24, 8, pp. 2579-2591, (2015)
[9]  
Zhang M, Muramatsu C, Zhou X R, Hara T, Fujita H., Blind image quality assessment using the joint statistics of generalized local binary pattern, IEEE Signal Processing Letters, 22, 2, pp. 207-210, (2015)
[10]  
Li Q L, Lin W S, Xu J T, Fang Y M., Blind image quality assessment using statistical structural and luminance features, IEEE Transactions on Multimedia, 18, 12, pp. 2457-2469, (2016)