No-Reference Quality Assessment for Multiply Distorted Images based on Deep Learning

Most of the existing algorithm of objective image quality evaluation are often for a single type of distortion, and the effect of multi-distortion image quality evaluation is poor. In this paper presents a no reference image quality assessment method based on phase congruency and convolution neural network, to evaluate the mixed distorted image. Firstly, the input image is divided into blocks and phase congruency, and then the quality score of the image is trained and predicted by using the convolution network. The convolutional network structure consists of 4 layers of convolutions, 3 layers of maximum pooling, and 2 fully connected layers. The experimental results on the LIVE mulitipy distortion quality evaluation database show that the proposed method has a good consistency between the image quality and the subjective quality score.