计算机工程与应用2019，Vol.55Issue(9)：26-32,7.DOI:10.3778/j.issn.1002-8331.1812-0351
双目标的CNN无参考图像质量评价方法
Double-Target CNN Image Quality Assessment Method
摘要
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 indi-cated 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.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Key words
no-reference image quality assessment/ double-target/ Convolutional Neural Network(CNN)/ feature learning/ sequentially分类
信息技术与安全科学引用本文复制引用
程晓梅,沈远彤..双目标的CNN无参考图像质量评价方法[J].计算机工程与应用,2019,55(9):26-32,7.基金项目
国家自然科学基金(No.61601417). （No.61601417）
