High-Pass Difference Features Based Image Quality Assessment

Authors

Wang Rui, 1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;;
Li Ping, 2. Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China;;
Bin Sheng, 1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;;
Congbin Qiao, 1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;;
Lizhuang Ma, 1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;;
Enhua Wu, 3. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;;4. Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau 999078, China;

Abstract

Abstract: Current methods of image quality assessment only can assess the quality of images under the same type of image distortion. In order to fix such weaknesses, this paper is designed based on the image features of natural scene statistics and proposes a new metric method using high-pass filter for detecting features. The approach computes locally the normalized luminance; selects features such as the difference of RGB channels via high-pass filter, image gradient, sharpness, contrast, etc.; and analyzes and gathers features in the metric method trained by logistic regression. Experimental results show that the proposed method can work efficiently under multiple distortion types and is significantly better than current no-reference image quality assessment methods under the test sets, which gather multiple distortion types.

Recommended Citation

Rui, Wang; Ping, Li; Sheng, Bin; Qiao, Congbin; Ma, Lizhuang; and Wu, Enhua (2019) "High-Pass Difference Features Based Image Quality Assessment," Journal of System Simulation: Vol. 31: Iss. 2, Article 8.
DOI: 10.16182/j.issn1004731x.joss.17DEA-001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss2/8

First Page

227

Revised Date

2016-12-28

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17DEA-001

Last Page

237

CLC

TP391.9

Recommended Citation

Wang Rui, Li Ping, Sheng Bin, Qiao Congbin, Ma Lizhuang, Wu Enhua. High-Pass Difference Features Based Image Quality Assessment[J]. Journal of System Simulation, 2019, 31(2): 227-237.

Corresponding Author

Bin Sheng,

DOI

10.16182/j.issn1004731x.joss.17DEA-001