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Volume 31 Issue 2
March  2016
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Qiaohong Li, Yu-Ming Fang, Jing-Tao Xu. A Novel Spatial Pooling Strategy for Image Quality Assessment[J]. Journal of Computer Science and Technology, 2016, 31(2): 225-234. DOI: 10.1007/s11390-016-1623-9
Citation: Qiaohong Li, Yu-Ming Fang, Jing-Tao Xu. A Novel Spatial Pooling Strategy for Image Quality Assessment[J]. Journal of Computer Science and Technology, 2016, 31(2): 225-234. DOI: 10.1007/s11390-016-1623-9
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A Novel Spatial Pooling Strategy for Image Quality Assessment

  • 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

Funds: 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.
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  • Author Bio:

    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.

  • Corresponding author:

    Yu-Ming Fang E-mail: fa0001ng@e.ntu.edu.sg

  • Received Date: December 17, 2015
  • Revised Date: February 09, 2016
  • Published Date: March 04, 2016
    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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    • References (24)
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