A Novel Spatial Pooling Strategy for Image Quality Assessment

• Published in: Journal of computer science and technology 2016-03, Vol.31 (2), p.225-234
• Main Authors: Li, Qiaohong, Fang, Yu-Ming, Xu, Jing-Tao
• Format: Article
• Language: English
• 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
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-016-1623-9

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.