A novel depth spatial-temporal consistency enhancement algorithm for high compression performance

In free viewpoint video system based on multiview video plus depth, inconsistency with depth video need to be eliminated to ensure high-quality virtual view generation and compression performance. The preprocessing method proposed can compensate both spatial and temporal depth information inaccuracy by using Bayesian probability model and Rival penalized competitive learning in Self-Organizing Maps. Firstly, each gray value in depth video is assigned to specific class after clustering. Then gradient filter is utilized in smoothing. Experiments show that the proposed algorithm reduced the bit rate ranging 7.97%-46.83% while ensuring quality of generated virtual viewpoint.