Light Field Image Quality Assessment Using Natural Scene Statistics and Texture Degradation

Light field image (LFI) now is becoming increasingly popular in immersive media applications. Unlike traditional 2D and 3D images, images taken by light field cameras can capture both angular and spatial information. However, the spatial and angular information of LFI is highly inter-twined with varying disparities, which poses a higher challenge to the quality assessment of LFI. To address this issue, this paper proposes a full-reference light field image quality assessment (LFIQA) index that attempts to disentangle the coupling information from macro-pixel image (MacPI) to accurately evaluate the entire LFI quality. The proposed framework can be divided into three steps. Firstly, the LFIs are converted into the MacPIs, and then the spatial and angular feature maps are disentangled by using the spatial, angular and epipolar plane image (EPI) convolutions in the MacPI mode. Secondly, the structural similarity (SSIM) maps are calculated between the disentangled feature maps of the original and distorted LFIs. Furthermore, the quality-aware features of LFIs are extracted on the SSIM maps by utilized local binary patterns (LBP) and natural scene statistics (NSS). Finally, support vector regression (SVR) is utilized to predict the qualities of LFIs. Extensive experiments show that the proposed model outperforms multiple classical and state-of-the-art methods.