Light Field Image Quality Assessment Using Contourlet Transform

Abstract

In this paper, a new full-reference image quality assessment (IQA) method for performing perceptual quality evaluation on the light field (LF) image is proposed, called the contourlet transform-based model (CTM). The LF image consists of a set of sub-aperture images (SAIs) that have similar image content but with small angular deviations due to different viewing angles. Hence, the abundant image details can be extracted from the SAIs. To fulfil this goal, the contourlet transform is used to extract the multi-scale spatial features of the reference and distorted SAIs, respectively. Based on our proposed IQA metric, the degree of similarity can be computed based on the above-measured quantities for arriving at the final IQA score of the distorted LF image under evaluation. Experimental simulation results obtained from the dense light fields datasets clearly show that the proposed CTM algorithm is more in line with the quality assessment of the LF images perceived by the human visual system (HVS) when compared with that of using other state-of-the-art IQA algorithms.