Spatial non-stationary correlation noise modeling for Wyner-Ziv error resilience video coding

Abstract

Most of the Wyner-Ziv (WZ) video coding schemes in literature model the correlation noise (CN) between original frame and side information (SI) by a given distribution whose parameters are estimated in an offline process. In this paper, an online CN modeling algorithm is proposed towards a more practical WZ-based error resilient video coding (WZ-ERVC). In ERVC scenario, the side-information is typically generated from the error concealed picture instead of bi-directional motion prediction. The proposed online CN modeling algorithm achieves the so-called classification gain by exploiting the spatially non-stationary characteristics of the motion field and texture. The CN between the source and error concealed SI is modeled by a Laplacian mixture model, where each mixture component represents the statistical distribution of prediction residuals and the mixing coefficients portray the motion vectors estimation error. Experimental results demonstrate significant performance gains both in rate and distortion versus the conventional Laplacian model.