Progressive correlation noise refinement for transform domain Wyner-Ziv video coding

Juan Song, Keyan Wang, Haiying Liu, Yunsong Li, Chengke Wu

Abstract: Correlation Noise Modeling (CNM) is a key factor to influence the performance of Distributed Video Coding (DVC). In most current CNM solutions, the distribution parameter is estimated based on the motion compensated residual frames and kept constant during the decoding process. A progressive correlation noise refinement method is proposed in this paper for transform domain Wyner-Ziv video coding to model the correlation noise more accurately, in which the estimated correlation noise is refined by using previously decoded bitplanes and quantization errors as bitplane decoding proceeds. The experimental results show that our proposed correlation noise refinement method could provide considerable bitrate savings and PSNR gains for transform domain Wyner-Ziv video coding system.