In this paper, we present a depth-perceived 3D visual saliency map and propose a no-reference stereoscopic image quality assessment (NR SIQA) algorithm using 3D visual saliency maps and convolutional neural network (CNN). Firstly, the 2D salient region of stereoscopic image is generated, and the depth saliency map is calculated, and then, they are combined to compute 3D visual saliency map by linear weighted method, which can better use depth and disparity information of 3D image. Finally, 3D visual saliency map, together with distorted stereoscopic pairs, is fed into a three-channel CNN to learn human subjective perception. We call proposed depth perception and CNN-based SIQA method DPCNN. The performances of DPCNN are evaluated over the popular LIVE 3D Phase I and LIVE 3D Phase II databases, which demonstrates to be competitive with the state-of-the-art NR SIQA algorithms.

在本文中，我们提出了一种深度感知的 3D 视觉显着图，并提出了一种使用 3D 视觉显着图和卷积神经网络 (CNN) 的无参考立体图像质量评估 (NR SIQA) 算法。首先生成立体图像的2D显着区域，计算深度显着图，然后将它们结合起来，通过线性加权的方法计算3D视觉显着图，可以更好地利用3D图像的深度和视差信息。最后，将 3D 视觉显着性图与扭曲的立体对一起输入三通道 CNN 以学习人类主观感知。我们将提出的深度感知和基于 CNN 的 SIQA 方法称为 DPCNN。DPCNN 的性能在流行的 LIVE 3D Phase I 和 LIVE 3D Phase II 数据库上进行评估，