No-Reference Stereoscopic Image Quality Assessment Based on The Visual Pathway of Human Visual System

Abstract

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the ‘what’ and ‘where’ visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.