›› 2016,Vol. 31 ›› Issue (2): 225-234.doi: 10.1007/s11390-016-1623-9

所属专题: Artificial Intelligence and Pattern Recognition Computer Graphics and Multimedia

• Special Section on Selected Paper from NPC 2011 •    下一篇

一种新的用于图像质量评价的空域池化方法

Qiaohong Li1, Yu-Ming Fang2*, Member, CCF, IEEE, and Jing-Tao Xu3   

  1. 1 School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;
    2 School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;
    3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications Beijing 100086, China
  • 收稿日期:2015-12-18 修回日期:2016-02-10 出版日期:2016-03-05 发布日期:2016-03-05
  • 通讯作者: Yu-Ming Fang E-mail:fa0001ng@e.ntu.edu.sg
  • 作者简介:Qiaohong Li received her B.E. and M.E. degrees in information and communication engineering from Beijing University of Posts and Telecommunications, Beijing, in 2009 and 2012 respectively. She is currently pursuing her Ph.D. degree with the School of Computer Engineering, Nanyang Technological University, Singapore. Her research interests include image quality assessment, speech quality assessment, computer vision, and visual perceptual modelling.
  • 基金资助:

    This work was supported by the National Natural Science Foundation of China under Grant No. 61571212 and the Natural Science Foundation of Jiangxi Province of China under Grant No. 20151BDH80003.

A Novel Spatial Pooling Strategy for Image Quality Assessment

Qiaohong Li1, Yu-Ming Fang2*, Member, CCF, IEEE, and Jing-Tao Xu3   

  1. 1 School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;
    2 School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;
    3 School of Information and Communication Engineering, Beijing University of Posts and Telecommunications Beijing 100086, China
  • Received:2015-12-18 Revised:2016-02-10 Online:2016-03-05 Published:2016-03-05
  • Contact: Yu-Ming Fang E-mail:fa0001ng@e.ntu.edu.sg
  • About author:Qiaohong Li received her B.E. and M.E. degrees in information and communication engineering from Beijing University of Posts and Telecommunications, Beijing, in 2009 and 2012 respectively. She is currently pursuing her Ph.D. degree with the School of Computer Engineering, Nanyang Technological University, Singapore. Her research interests include image quality assessment, speech quality assessment, computer vision, and visual perceptual modelling.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant No. 61571212 and the Natural Science Foundation of Jiangxi Province of China under Grant No. 20151BDH80003.

传统全参考图像质量评价方法通常包括两个步骤:第一步,通过比较参考图像和失真图像的局部特征,得到图像像素的质量评价分数,建立局部质量图。第二步,通过空域池化方法,从局部质量图融合计算得到一个评价分值。本文通过分析质量分布和评价分值的关系,提出了一种新型的空域池化方法。通过分析,我们发现评价分值依赖于质量分布。该方法从局部质量图提取质量直方图和统计特征来描述像素质量的空域分布。最后采用支持向量机学习像素质量分布和整体评价分值的映射关系。在三个图像数据库上的大量实验结果表明,本文提出的空域池化方法能够显著地提高现有图像质量评价方法的性能,与主观质量评价结果更加吻合。

Abstract: 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.

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