Improving performance of distributed video coding by consecutively refining of side information and correlation noise model

Distributed video coding (DVC) is built on distributed source coding (DSC) principles where the video statistics are exploited, partly or fully, at the decoder instead of the encoder. In theory, DVC scheme is proved that there is no performance loss when compared to predictive video coding. However, its practical implementation has a large gap to achieve the theoretically optimum performance. The DVC coding efficiency depends mainly on creating the side information (SI) - a noisy version of original Wyner-Ziv frame (WZF) at the decoder, and modeling the correlation noise - the difference between the original WZF and corresponding SI. Performance of the DVC scheme will be improved if the SI and correlation noise are estimated as accurately as possible. So, this paper proposes a method to enhance the quality of SI and also correlation noise model by using information in decoded WZFs during the decoding process. Initial SI which is generated by Motion-Compensated Temporal Interpolation (MCTI) and previously decoded keyframes (KF) will be used as reference frames to consecutively refine the side information after each bitplane is decoded. The experimental results show that performance of the distributed video coder is significantly improved by using this method.