Blind Image Quality Assessment for Stereoscopic Images via Deep Learning

Stereoscopic image quality assessment is an effective way to evaluate the performance of stereoscopic video systems, but how to utilize human visual characteristics effectively is still a research focus in stereoscopic image quality assessment. In this paper, a blind image quality assessment method for stereoscopic images is proposed via deep learning. The proposed method is composed of two stages: training and testing. In the training stage, Gabor filter is applied to the left and right distorted images respectively, and natural statistical features under different scales and directions are extracted to act as monocular features. Then, left and right images are fused to construct a cyclopean map, and histograms of oriented gradient features are extracted from the cyclopean map to act as binocular features. Finally, a regression model between features and subjective scores is established via deep belief network. In the testing stage, based on the established regression model, left and right image quality scores are predicted and fused to get the final stereoscopic image quality score. Experimental results show that the proposed method is effective for both symmetrical and asymmetrical stereoscopic image databases, and can achieve high consistent alignment with subjective assessment.