Abstract: Objective evaluation of image quality is widely used in many image processing tasks. A non-reference image quality evaluation algorithm is proposed based on small parallel-mode convolutional neural networks under deep learning technology. Convolution operation and parallel multi-scale input could learn not only rich feature, but also subtle feature. Firstly, the Gaussian image pyramid is used to obtain different scale distorted images as the input of 4 small-scale single-layer convolutional neural networks. After convolution and pooling, 4 feature vectors are output, and the learned feature vectors are merged and then mapped into image quality prediction scores through fully connected regression. Para- meters are optimized through two serial stages to improve the accuracy of the model. Experimental test- ing results show that the designed small network model is effective, and the proposed algorithm has higher performance than the current comparative algorithms and has good stability and strong generalization ability.

Key words: convolutional neural network, image quality evaluation, multi-scale image, fully connected regression, deep learning