Stereoscopic image quality assessment considering visual mechanism and multi-loss constraints

Abstract

In this paper, a convolutional neural network (CNN) with multi-loss constraints is designed for stereoscopic image quality assessment (SIQA). A stereoscopic image not only contains monocular information, but also provides binocular information which is as identically crucial as the former. So we take the image patches of left-view images, right-view images and the difference images as the inputs of the network to utilize monocular information and binocular information. Moreover, we propose a method to obtain proxy label of each image patch. It preserves the quality difference between different regions and views. In addition, the multiple loss functions with adaptive loss weights are introduced in the network, which consider both local features and global features and constrain the feature learning from multiple perspectives. And the adaptive loss weights also make the multi-loss CNN more flexible. The experimental results on four public SIQA databases show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.