基于高通差异性特征的图像质量评估方法

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

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (2): 227-237.doi: 10.16182/j.issn1004731x.joss.17DEA-001

基于高通差异性特征的图像质量评估方法

王睿¹, 李平², 盛斌^{1, *}, 谯从彬¹, 马利庄¹, 吴恩华^{3, 4}

1. 上海交通大学计算机科学与工程系,上海 200240;
2. 澳门科技大学资讯科技学院,澳门 999078;
3. 中国科学院软件研究所计算机科学国家重点实验室,北京 100190;
4. 澳门大学科技学院电脑及资讯科学系,澳门 999078

收稿日期:2016-08-14 修回日期:2016-12-28 出版日期:2019-02-15 发布日期:2019-02-15
作者简介:王睿(1992-),男,江苏南京,硕士生,研究方向为图像质量评估。
基金资助:
国家自然科学基金(61572316, 61671290),国家重点研发计划(2016YFC1300302),香港研究资助局杰出青年学者计划(28200215),上海市科学技术委员会(16DZ0501100),国家863计划(2015AA015904),浙江大学CAD&CG国家重点实验室开放课题(A1401)

High-Pass Difference Features Based Image Quality Assessment

Wang Rui¹, Li Ping², Sheng Bin^{1, *}, Qiao Congbin¹, Ma Lizhuang¹, Wu Enhua^{3, 4}

1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China;
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

Received:2016-08-14 Revised:2016-12-28 Online:2019-02-15 Published:2019-02-15

摘要/Abstract

摘要： 现有的图像质量评估只能判断单一失真方式下失真图像的质量优劣。为了改进这一缺点,根据自然场景统计信息的图像特征,提出基于高通滤波下RGB差异性的图像质量评估方法,通过局部归一化亮度,提取RGB通道差异性、图像梯度、图像锐度、及图像对比度等特征,利用逻辑回归训练,最终得到无参考图像质量评估模型。实验结果表明,方法对各类型失真图像质量评估准确率较高,特别对多种失真类型混合的测试集时,具有明显优势。

关键词: 图像质量评估, 无参考型, 逻辑回归, 自然场景统计

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.

Key words: image quality assessment, no-reference, logistic regression, natural scene statistics

中图分类号:

TP391.9

王睿, 李平, 盛斌, 谯从彬, 马利庄, 吴恩华. 基于高通差异性特征的图像质量评估方法[J]. 系统仿真学报, 2019, 31(2): 227-237.

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.

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基于高通差异性特征的图像质量评估方法

High-Pass Difference Features Based Image Quality Assessment

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