Blind image quality assessment (BIQA) of distorted stereoscopic pairs without referring to the undistorted source is a challenging problem, especially when the distortions in the left- and right-views are asymmetric. 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 propose a binocular rivalry inspired multi-scale model to predict the quality of stereoscopic images from that of the single-view images without referring to the original left- and right-view images. We apply this blind 2D-to-3D quality prediction model on top of ten stateof-the-art base 2D-BIQA algorithms for 3D-BIQA. Experimental results show that the proposed 3D-BIQA model, without explicitly identifying image distortion types, successfully eliminates the prediction bias, leading to significantly improved quality prediction performance. Among all the base 2D-BIQA algorithms, BRISQUE and M3 archive excellent tradeoffs between accuracy and complexity.
Jiheng Wang, Qingbo Wu, Abdul Rehman, Shiqi Wang, Zhou Wang, "Blind Quality Prediction of Stereoscopic 3D Images" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Human Vision and Electronic Imaging, 2017, pp 70 - 76, https://doi.org/10.2352/ISSN.2470-1173.2017.14.HVEI-379