摘要
提出了一种基于图像融合的无参考立体图像质量评价算法。该算法利用小波变换分解重构立体图像的左右视图并融合在一幅图像中,归一化处理融合图像的亮度系数,均衡各部分亮度并保留融合图像的结构信息,使用卷积神经网络进行特征提取和回归预测。实验结果表明,所提方法的预测得分与人类主观评价得分具有很好的一致性。
A no-reference stereo image quality assessment algorithm based on image fusion is proposed.The algorithm reconstructs the left and right views of the stereo image by wavelet transform and fuses them into one image.The luminance coefficient of the fused image is normalized,which keeps the brightness of each part in balance and preserves the structural information of the fused image.Finally the convolutional neural network is used to extract feature and predict regression.The experimental results show that the predicted scores of the proposed method are in good agreement with the human subjective assessment scores.
作者
黄姝钰
桑庆兵
Huang Shuyu;Sang Qingbing(Jiangsii Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,College of hiteriiet of Things Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第7期122-130,共9页
Laser & Optoelectronics Progress
基金
江苏省自然科学基金(BK20171142)
关键词
图像处理
立体图像质量评价
图像融合
小波变换
亮度系数归一化:卷积神经网络
image processing
stereo image quality assessment
image fusion
wavelet transform
normalized luminance coefficient
convolutional neural network
