Blind Stereo Image Quality Evaluation Based on Convolutional Network and Saliency Weighting

Abstract

With the rapid development of stereo image applications, there is an increasing demand to develop a versatile tool to evaluate the perceived quality of stereo images. Therefore, in this study, a blind stereo image quality evaluation (SIQE) algorithm based on convolutional network and saliency weighting is proposed. The main network framework used by the algorithm is the quality map generation network, which is used to train the distortion image dataset and quality map label to obtain an optimal network framework. Finally, the left view, right view, and cyclopean view of the stereo image are used as inputs to the network frame, respectively, and then weighted fusion for the final stereo image quality score. The experimental results reveal that the proposed SIQE algorithm can improve the accuracy of the image quality prediction and prediction score to a certain extent and has good generalization ability.

Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network

“…Compared with two-dimensional (2D) images and videos, 3D images and videos have a variety of detailed depth information, and provide viewers with an excellent visual experience [7][8][9][10][11][12]. The display effect of 3D images is mainly affected by the elements used to produce 3D contents, including brightness, chroma, contrast, saturation, and parallax [13][14][15][16][17][18]. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging [19][20][21][22][23].…”