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Proceedings Paper

SPCA: a no-reference image quality assessment based on the statistic property of the PCA on nature images
Author(s): Yun Zhang; Chao Wang; Xuanqin Mou
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Paper Abstract

Despite the acceptable performance of current full-reference image quality assessment (IQA) algorithms, the need for a reference signal limits their application, and calls for reliable no-reference algorithms. Most no-reference IQA approaches are distortion specific, aiming to measure image blur, JPEG blocking or JPEG2000 ringing artifacts respectively. In this paper, we proposed a no-reference IQA algorithm based on the statistic property of principal component analysis on nature image, named SPCA, which does not assume any specific type of distortion of the image. The method gets statistics of discrete cosine transform coefficients from the distort image’s principal components. Those features are trained by 􀟥-support vector regression method and finally test on LIVE database. The experimental results show a high correlation with human perception of quality (averagely over 90% by scores of SROCC), which is fairly competitive with the existing no-reference IQA metrics.

Paper Details

Date Published: 4 February 2013
PDF: 8 pages
Proc. SPIE 8660, Digital Photography IX, 86600K (4 February 2013); doi: 10.1117/12.2008599
Show Author Affiliations
Yun Zhang, Xi'an Jiaotong Univ. (China)
Chao Wang, Xi'an Jiaotong Univ. (China)
Xuanqin Mou, Xi'an Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 8660:
Digital Photography IX
Nitin Sampat; Sebastiano Battiato, Editor(s)

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