关键词: 无参考图像质量评价, 双目标, 卷积神经网络, 特征学习, 次序

Abstract: In order to effectively extract the image quality features highly correlated with human visual perception, the double-target convolutional neural network is proposed in this paper, which can estimate image distortion type and quality scores. The network structure trained the distortion type features and quality features of the image sequentially to make the network more fully excavate the image degradation type information and strengthen its auxiliary role in the quality score estimation task, and then improve its learning ability for image quality features. Simultaneously, the experiment indicated the two-step feature extraction method can accelerate the convergence of the network. Comparison experiments are carried out on the standard image quality evaluation database LIVE and TID2008, which show the algorithm can accurately evaluate the image quality scores and recognition distortion types, obviously better than other evaluation methods.

Key words: no-reference image quality assessment, double-target, Convolutional Neural Network（CNN）, feature learning, sequentially