Stereoscopic 3D (S3D) visual quality prediction (VQP) is used to predict human perception of visual quality for S3D images accurately and automatically. Unlike that of 2D VQP, the quality prediction of S3D images is more difficult owing to complex binocular vision mechanisms. In this study, inspired by the binocular fusion and competition of the binocular visual system (BVS), we designed a blind deep visual quality predictor for S3D images. The proposed predictor is a multi-layer fusion network that fuses different levels of features. The left- and right-view sub-networks use the same structure and parameters. The weights and qualities for the left- and right-view patches of S3D images can be predicted. Furthermore, training patches with more saliency information can improve the accuracy of prediction results, which also make the predictor more robust. The LIVE 3D Phase I and II datasets were used to evaluate the proposed predictor. The results demonstrate that the performance of the proposed predictor surpasses most existing predictors on both asymmetrically and symmetrically distorted S3D images.

立体3D（S3D）视觉质量预测（VQP）用于为人类准确，自动地预测S3D图像的视觉质量感知。与2D VQP不同，由于复杂的双目视觉机制，S3D图像的质量预测更加困难。在这项研究中，受双目融合和双目视觉系统（BVS）竞争的启发，我们为S3D图像设计了一种盲目的深视觉质量预测器。提出的预测器是融合不同级别功能的多层融合网络。左视图和右视图子网使用相同的结构和参数。可以预测S3D图像的左右视图块的权重和质量。此外，训练具有更多显着性信息的补丁可以提高预测结果的准确性，这也使预测器更加健壮。LIVE 3D第一和第二阶段数据集用于评估建议的预测变量。结果表明，在不对称和对称失真的S3D图像上，拟议的预测器的性能均超过大多数现有的预测器。