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.
The authors have been conducting research on cross-modal perception employing sensory integration in which participants perceive tactile sensation from stereoscopic (3D) images. The pseudo-haptic system enables the phenomenon of subtle tactile sensation by spatial and temporal synchronization with 3D images without any physical contact. In this study, a 3D image of an object was presented using a binocular see-through head-mounted display, and participants moved their forearms as if touching the viewed object. Myoelectric potentials were measured during experiencing a subtle tactile sensation by the forearm movements. From the results of the experiment, a decrease of myoelectric potential and extension of movement time were found with increase of intensity of the pseudo-haptic sensation.