Reduced-Reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics and Structural Degradation

Abstract

Perceptual stereo image quality assessment (SIQA) aims to design computational models to measure the stereo image quality in accordance with human opinions. In this paper, a novel reduced-reference (RR) SIQA is proposed by characterizing the statistical and perceptual properties of the stereo image in both the spatial and gradient domains. To be specific, in the spatial domain, we extract the parameters of the generalized Gaussian distribution fits of luminance wavelet coefficients to form the underlying features. In the gradient domain, the modified gradient magnitudes maps are generated by jointly considering human visual system's contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. Afterward, perceptual features are extracted based on the entropy of discrete wavelet transform coefficients of modified gradient magnitudes. Furthermore, we consolidate the left and right features into a single set of features per stereo image pair. Finally, the qualities of both the spatial and gradient domains are combined to obtain the overall quality of stereo image. Extensive experiments performed on popular data sets demonstrate that the proposed RR-SIQA method achieves highly competitive performance as compared with the state-of-the-art RR-SIQA models as well as full-reference ones for both symmetric and asymmetric distortions.