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Proceedings Paper

Learning based saliency weighted structural similarity
Author(s): Xiaoliang Sun; Xiaolin Liu
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Paper Abstract

Image quality assessment (IQA) is a critical issue in image processing applications, but commonly used criterions for image quality assessment do not map well with perceived quality. The recently proposed structural similarity (SSIM) is regarded as an excellent work in image quality assessment criterions, but it only consider local information and ignore some important global concepts. Based on the SSIM image quality assessment criterion and the detection of visual saliency in image, this paper proposes a learning based saliency weighted structural similarity IQA criterion. The algorithm combines the SSIM index and saliency map in a machine learning framework to learn a mapping from these features to perceived image quality. Experiments on a standard image quality assessment database show that our algorithm performs better than commonly used criterions, and our algorithm captures results which correlate well with subjective judgments of image quality.

Paper Details

Date Published: 15 November 2011
PDF: 7 pages
Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 83351H (15 November 2011); doi: 10.1117/12.917545
Show Author Affiliations
Xiaoliang Sun, National Univ. of Defense Technology (China)
Xiaolin Liu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 8335:
2012 International Workshop on Image Processing and Optical Engineering

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