计算机应用与软件2017，Vol.34Issue(1)：175-179,5.DOI:10.3969/j.issn.1000-386x.2017.01.032
一种改进的卷积神经网络的无参考JPEG2000图像质量评价方法
A NO-REFERENCE JPEG2000 IMAGE QUALITY ASSESSMENT VIA IMPROVED CONVOLUTIONAL NEURAL NETWORK
摘要
Abstract
The existing image quality evaluation model for JPEG2000 compression image distortion upon evaluation is not very ideal.In view of this, a JPEG2000 compressed image quality evaluation method based on improved CNN framework is put forward.The model is consisted of one convolutional layer with 20 convolution kernels, one pooling layer with the max, medium and min pooling, one fully connected layer with 1200 ReLU units and one output node.Using the max, medium and min pool structure to extract the typical features in the image effectively.Experimental results show that the proposed method is consistent with the subjective score better in the LIVE library.关键词
卷积神经网络/深度学习/无参考图像质量评价Key words
Convolutional neural network (CNN)/Deep learning/No-reference Image quality assessment分类
信息技术与安全科学引用本文复制引用
朱睿,李朝锋..一种改进的卷积神经网络的无参考JPEG2000图像质量评价方法[J].计算机应用与软件,2017,34(1):175-179,5.基金项目
国家自然科学基金项目(61170120) （61170120）
教育部优秀人才计划项目(NCET-12-0881). （NCET-12-0881）
