A Novel Spatial Pooling Strategy for Image Quality Assessment

A variety of existing image quality assessment （IQA） metrics share a similar two-stage framework： at the first stage, a quality map is constructed by comparison between local regions of reference and distorted images; at the second stage, the spatial pooling is adopted to obtain overall quality scor...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 31; no. 2; pp. 225 - 234
Main Authors Li, Qiaohong, Fang, Yu-Ming, Xu, Jing-Tao
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2016
Springer Nature B.V
School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore%School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China%School of Information and Communication Engineering, Beijing University of Posts and Telecommunications Beijing 100086, China
Subjects
Algorithms
Artificial Intelligence
Assessments
Computer engineering
Computer Science
Correlation
Data Structures and Information Theory
Distortion
Human performance
Human subjects
Image processing systems
Image quality
Information Systems Applications (incl.Internet)
Quality assessment
Regression analysis
Regular Paper
Science
Software Engineering
Standard deviation
Statistical analysis
Strategy
Studies
Support vector machines
Theory of Computation
图像质量评价
支持向量回归
整体质量
统筹方法
统计分析
评价空间
质量分布
质量评估
support vector regression
structural similarity
image quality assessment
statistical pooling
spatial pooling
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Summary:A variety of existing image quality assessment （IQA） metrics share a similar two-stage framework： at the first stage, a quality map is constructed by comparison between local regions of reference and distorted images; at the second stage, the spatial pooling is adopted to obtain overall quality score. In this work, we propose a novel spatial pooling strategy for image quality assessment through statistical analysis of the quality map. Our in-depth analysis indicates that the overall image quality is sensitive to the quality distribution. Based on the analysis, the quality histogram and statistical descriptors extracted from the quality map are used as input to the support vector regression to obtain the final objective quality score. Experimental results on three large public IQA databases have demonstrated that the proposed spatial pooling strategy can greatly improve the quality prediction performance of the original IQA metrics in terms of correlation with human subjective ratings.
Bibliography:A variety of existing image quality assessment （IQA） metrics share a similar two-stage framework： at the first stage, a quality map is constructed by comparison between local regions of reference and distorted images; at the second stage, the spatial pooling is adopted to obtain overall quality score. In this work, we propose a novel spatial pooling strategy for image quality assessment through statistical analysis of the quality map. Our in-depth analysis indicates that the overall image quality is sensitive to the quality distribution. Based on the analysis, the quality histogram and statistical descriptors extracted from the quality map are used as input to the support vector regression to obtain the final objective quality score. Experimental results on three large public IQA databases have demonstrated that the proposed spatial pooling strategy can greatly improve the quality prediction performance of the original IQA metrics in terms of correlation with human subjective ratings.
image quality assessment, spatial pooling, statistical pooling, support vector regression, structural similarity
11-2296/TP
ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-016-1623-9