Efficient estimation of disparity statistics and their use as a predictor for perceived 3D video scene quality

Excessive depth perception in 3D video is one of the major factors that causes discomfort to the viewer and that can decrease the viewer’s quality perception of 3D video. With the idea of real-time quality control of 3D videos, we proposed an edge-based sparse disparity estimation algorithm with a novel similarity metric. The comparative assessment with other four state-of-the-art similarity metrics, implemented within the proposed edge-based disparity estimator, showed higher performance for the novel metric. User tests are conducted to assess the relation between certain disparity statistics and user perception of 3D scene quality that is a retrospective subjective experience of quality. Subjective tests indicate that the viewer discomfort can be predicted best by using maximum and slew rate of 95 percentile scene disparities together.