No-Reference Stereo Image Quality Assessment Based on Transfer Learning  

作　　者：Lixiu Wu Song Wang Qingbing Sang 

机构地区：[1]Jiangsu Key Construction Laboratory of IoT Application Technology(Wuxi Taihu University),Wuxi,214000,China [2]Pactera Yuanhui Technology(Wuxi)Co.,LTD.,Wuxi,214000,China [3]Jiangnan University,Wuxi,214000,China

出　　处：《Journal of New Media》2022年第3期125-135,共11页新媒体杂志（英文）

摘　　要：In order to apply the deep learning to the stereo image quality evaluation,two problems need to be solved:The first one is that we have a bit of training samples,another is how to input the dimensional image’s left view or right view.In this paper,we transfer the 2D image quality evaluation model to the stereo image quality evaluation,and this method solves the first problem;use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem.At the same time,the input image is preprocessed by phase congruency transformation,which further improves the performance of the algorithm.The structure of the deep convolution neural network consists of four convolution layers and three maximum pooling layers and two fully connected layers.The experimental results on LIVE3D image database show that the prediction quality score of the model is in good agreement with the subjective evaluation value.

关 键 词：NO-REFERENCE stereo image quality assessment convolution neural network transfer learning phase congruency transformation image fusion 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]