In order to evaluate different kinds of distorted images efficiently

a novel general-purpose blind/no-reference image quality assessment is proposed

which combines perceptual features with spatial natural statistics features to construct an image quality assessment model. Four perceptual features-phase congruency entropy

mean phase congruency

mean gradient

and entropy of the distorted images are selected beside the 36 spatial natural statistics features of sharp patches.features.Support Vector Machine Regression(SVR)is adopted to build the relationship between image features and quality scores

yielding a measure of image quality. Experimental results in the LIVE database show that the proposed method accords closely with human subjective judgment.It has good robustness and short running time. The proposed method has a good performance.The selected features are rational and the learning method is effective.