Combining Tensor Slice and Singular Value for Blind Light Field Image Quality Assessment

Abstract

Light field image (LFI) collects radiance from rays in different directions, offers powerful capabilities for immersive media and computer vision. As the high-dimensional data, LFI suffers from spatial as well as angular information distortions in its processing, which brings new challenges to image quality assessment (IQA). Based on the strong ability of tensor about representing high-dimensional data and distortion characteristics of LFI, this paper proposes a method of combining tensor slice and singular value for blind light field image quality assessment (TSSV-LFIQA) to effectively evaluate the quality of LFI content. Specifically, five-order tensor representation of LFI is firstly defined which contains light ray intensity, angular information and color information of the LFI. Secondly, the first slice sharpness measurement and the other slice information distribution are used to describe the tensor slice spatial feature (TSSF) of the LFI. Moreover, singular value angular feature (SVAF) is also proposed to measure the angular consistency of LFI by further unfolding the five-order tensor of LFI and analyzing the percentage of singular values. The experimental results show that benefiting from the combination of TSSF and SVAF, the proposed TSSV-LFIQA method is statistically superior to the existing IQA methods, and matches well with human subjective opinions.