Objective quality assessment of distorted stereoscopic images 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 paper, we first build a database that contains both single-view and symmetrically and asymmetrically distorted stereoscopic images. We then carry out a subjective test, where we find that the quality prediction bias of the asymmetrically distorted images could lean toward opposite directions (overestimate or underestimate), depending on the distortion types and levels. Our subjective test also suggests that eye dominance effect does not have strong impact on the visual quality decisions of stereoscopic images. Furthermore, we develop an information content and divisive normalization-based pooling scheme that improves upon structural similarity in estimating the quality of single-view images. Finally, we propose a binocular rivalry-inspired multi-scale model to predict the quality of stereoscopic images from 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 the stereoscopic images.
Objective quality assessment of stereoscopic 3D video is challenging but highly desirable, especially in the application of stereoscopic video compression and transmission, where useful quality models are missing, that can guide the critical decision making steps in the selection of mixed-resolution coding, asymmetric quantization, and pre- and post-processing schemes. Here we first carry out subjective quality assessment experiments on two databases that contain various asymmetrically compressed stereoscopic 3D videos obtained from mixed-resolution coding, asymmetric transform-domain quantization coding, their combinations, and the multiple choices of postprocessing techniques. We compare these asymmetric stereoscopic video coding schemes with symmetric coding methods and verify their potential coding gains. We observe a strong systematic bias when using direct averaging of 2D video quality of both views to predict 3D video quality. We then apply a binocular rivalry inspired model to account for the prediction bias, leading to a significantly improved full reference quality prediction model of stereoscopic videos. The model allows us to quantitatively predict the coding gain of different variations of asymmetric video compression, and provides new insight on the development of high efficiency 3D video coding schemes.
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