4 February 2013 SPCA: a no-reference image quality assessment based on the statistic property of the PCA on nature images
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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.
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Yun Zhang, Chao Wang, Xuanqin Mou, "SPCA: a no-reference image quality assessment based on the statistic property of the PCA on nature images", Proc. SPIE 8660, Digital Photography IX, 86600K (4 February 2013); doi: 10.1117/12.2008599; https://doi.org/10.1117/12.2008599
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