Quality prediction of asymmetrically distorted stereoscopic images from single views
Objective quality assessment of distorted stereoscopic images is a challenging problem. Existing studies suggest that simply averaging the quality of the left- and right-views well predicts the quality of symmetrically distorted stereoscopic images, but generates substantial prediction bias when applied to asymmetrically distorted stereoscopic images. In this study, we first carry out a subjective test, where we find that the prediction bias could lean towards opposite directions, largely depending on the distortion types. We then develop an information-content and divisive normalization based pooling scheme that improves upon SSIM in estimating the quality of single view images. Finally, we propose a binocular rivalry inspired model to predict the quality of stereoscopic images based on that of the single view images. Our results show that the proposed model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction of stereoscopic images.