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A no reference image quality assessment method for JPEG2000

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3 Author(s)
Jingchao Zhou ; Inst. of Autom., Chinese Acad. of Sci., Beijing ; Baihua Xiao ; Qiudan Li

This paper presents a novel no reference method to assess image quality. Firstly, the image is divided into many blocks. Textured blocks are selected and their amplitude fall-off curves are employed for quality prediction based on natural scene statistics. Secondly, projections of wavelet coefficients between adjacent scales with the same orientation are utilized to measure the positional similarity. At last, general regression neural network is adopted to conduct quality prediction according to features from above two aspects. The performance of our method is evaluated on a public data set and experimental results confirm its effectiveness.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008

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