Fast Disparity Estimation and Mode Decision for Multi-view Video Coding

Disparity estimation and mode decisions are key techniques in multi-view video coding (MVC) which could improve the compression efficiency when the computational complexity increasing greatly. Based on Kalman filtering, a novel fast disparity estimation and mode decision algorithm is presented in this paper. We firstly built a autoregressive (AR) model of disparity vectors on the basis of spatio-temporal correlation so as to achieve a preliminary result of disparity estimation. Furthermore, the Kalman filter is utilized to optimize and improve the estimation speed. Moreover, an effective reliability judgment method for mode prediction is presented, with which, a more precious mode prediction result can be obtained and the selected range of coding mode is effectively reduced to achieve low complexity mode decision. The experimental results show that the computational complexity is significantly reduced while the compression efficiency is still maintained.