Latitude and binocular perception based blind stereoscopic omnidirectional image quality assessment for VR system

Abstract

With the rapid development of 5G technologies, it is possible to perform high-speed transmission of virtual reality (VR) visual contents. As one of techniques related to VR visual contents, omnidirectional image/video quality assessment has been becoming one of important issues. In this paper, based on latitude characteristics of omnidirectional image and binocular perception of human visual system, we propose a new blind stereoscopic omnidirectional image quality assessment (BSOIQA) method. Firstly, with the latitude characteristics of omnidirectional image, an improved omnidirectional image saliency model is established to estimate the visual behavior of users. Secondly, to describe the perceptual characteristics of the left and right views of stereoscopic omnidirectional image (SOI), Weber local descriptor is used to obtain differential excitation image and orientation image. The differential excitation image is combined with saliency map to obtain the statistical features of quantized orientation image, and multi-scale statistical features are extracted from the left and right views of SOI. Thirdly, tensor decomposition is used to fuse the left and right views of SOI to extract binocular perception features. Dual-tree dual-density complex wavelet transform, image information entropy and singular value decomposition are used to extract binocular fusion features, and the latitude characteristics of SOI are taken into account in image blocking. The gradient map of the absolute difference image of the left and right views of SOI is calculated, and its histogram statistical features are extracted to represent binocular rivalry. In addition, the statistical characteristics of the binocular product image are used to represent the local structure information of binocular perception. Finally, the feature vector of SOI is created with feature screening and used to predict the objective quality of SOI, and an SOI subjective assessment dataset (NBU-SOID) is also established to evaluate the proposed BSOIQA method. Experimental results on NBU-SOID and other databases show that the proposed BSOIQA method is in better consistence with human visual perception, compared with the other state-of-the-art methods.