Pseudo Light Field Image and 4D Wavelet-Transform-Based Reduced-Reference Light Field Image Quality Assessment

Reduced-reference light field image (LFI) quality assessment (RR LFIQA) automatically assesses image quality with only partial information about the reference LFI is available. Existing RR LFIQA has difficulty extracting effective RR information and perceptual features to represent the LFI quality. In this article, we propose an RR LFIQA model based on pseudo LFI (PLFI) and four-dimensional (4D) wavelet transform. To extract RR information related to LFI perceptual quality, a PLFI is created as the RR information of the LFI using a view synthesis algorithm. Considering that the high-dimensional characteristics of the PLFI, 4D wavelet transform is used to decompose the original and distorted PLFIs. The 4D wavelet transform essentially performs a continuous 1D wavelet transform for the 4D signal to enable the local 4D structure of the PLFIs to be characterized effectively in the 4D wavelet domain. A novel spatial-angular weighting strategy is proposed to describe the importance of each location for quality evaluation, to further improve the performance of the proposed method. Experimental results on four benchmark datasets show that the proposed model performs better than the representative 2DIQA and LFIQA models.