Joint Statistical Models for No-Reference Stereoscopic Image Quality Assessment

Abstract

The recent advances in 3D acquisition and display technologies have led to the use of stereoscopy for a wide range of applications. The quality assessment of such stereo data becomes of great interest especially when the reference image is not available. For this reason, we propose in this paper a no-reference 3D image quality assessment algorithm based on joint statistical modeling of the wavelet subband coefficients of the stereo pairs. More precisely, we resort to bivariate and multivariate statistical modeling of the texture images to build efficient statistical features. These features are then combined with the depth ones and used to predict the quality score based on machine learning tools. The proposed methods are evaluated on LIVE 3D database and the obtained results show the good performance of joint statistical modeling based approaches.