Local and global sparse representation for no-reference quality assessment of stereoscopic images

No-reference/blind quality assessment of stereoscopic 3D images is much more challenging than 2D images due to the poor understanding of binocular vision. In this paper, we propose a BLind Quality Evaluator for stereoscopic 3D images by learning Local and Global Sparse Representations (BLQELGSR). Specifically, at the training stage, we first construct a large-scale training set by simulating some common distortions that are likely encountered by stereoscopic images, and propose a multi-modal sparse representation framework to characterize the relationship between the feature and quality spaces for all sources of information from left, right and cyclopean views in local and global manners. At the testing stage, based on the derived 3D quality prediction framework, the local and global quality scores from different sources are predicted and combined to drive a final 3D quality score. Experimental results on three 3D image quality databases show that in comparison with the existing methods, the devised BLQELGSR can achieve better prediction performance to be in line with subjective assessment.