No-reference Image Quality Assessment with Learning Phase Congruency Feature
Li Chao-feng①② Tang Guo-feng① Wu Xiao-jun① Ju Yi-wen②
①(School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China) ②(Key Laboratory of Computational Geodynamics, CAS, Colledge of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China)
Abstract：In order to assess multi distorted types of image quality effectively, a new no reference image quality assessment method is proposed, which uses General Regression Neural Network (GRNN) model to predict image quality score by learning phase congruency feature. In this method, three images, namely the Phase Congruency (PC) image, the maximum moment of PC covariance and the minimum moment of PC covariance image, are produced by the phase congruency model. Secondly the gradient entropy of the three reproduced images, the gradient mean value and the gradient entropy of the original image are computed by gray-level gradient co-occurrence matrix model, and the mean value of the three images are also calculated. At last all above eight features are fed to GRNN to learn, and predict image quality score. Experimental results demonstrate our algorithm is more consistent with human subjective scores and moreover has credible generalization.
2013, Vol. 35 
