Improvement of viewing experience on stereoscopic image guided by human stereo vision

Recent 3D visual quality assessment methods still have difficulties in providing the best viewing experience from the viewer’s perspective due to the ambiguous understanding of human stereo vision. One of the key reasons is that the disparity gradient, which affects human depth perception, is hard to control for the input stereo image pair. In this paper, we mathematically formulated the human disparity gradient and optimized the disparity gradients for each stereo image pair. Considering that the disparity gradient needs to be limited to a specific range to satisfy the human visual preference and comfortableness, we proposed a new quantitative definition of disparity gradient and trained the optimal disparity gradients were learned from the pilot study to enhance the viewing experience. Extensive subjective evaluations have demonstrated the competitiveness of this proposed method for the improvement of the viewing experience.