Natural image statistics based 3D reduced reference image quality assessment in contourlet domain

Designing a reliable and generic perceptual quality metric is a challenging issue in three-dimensional (3D) visual signal processing. In many practical 3D application scenarios, the original stereoscopic images (i.e., the reference image) cannot be fully accessed, leaving difficulties to quality assessment. To handle this problem, 3D reduced-reference image quality assessment (RRIQA) metrics have been investigated, which only extract small amount of information from the original stereoscopic images. In this paper, we propose a novel 3D RRIQA metric based on 3D natural image statistics in contourlet domain. In this metric, the Gaussian scale mixtures (GSM) model is employed to normalize the coefficients in the contourlet subband of luminance image and disparity map of the 3D images. After divisive normalization transform, we find that the marginal distribution of the coefficients is approximately Gaussian distributed. Based on these investigations, the standard derivations of the fitted Gaussian distribution are determined as the feature parameters in our metric for each contourlet subband. Then, the feature similarity index is employed to measure the 3D visual quality at the receiver side without accessing the reference stereoscopic images. Experimental results demonstrate that the proposed metric has good consistency with 3D subjective perception of human, and can be implemented in many end-to-end 3D video systems.