An Auto-Regressive Model for Improved Low Delay Distributed Video Coding

In this paper, an auto-regressive (AR) model is proposed to generate the side information for low-delay distributed video coding (DVC). The side information generation of current Wyner-Ziv (WZ) frame t consists of two forward AR interpolations. First, each pixel within the rebuilt frame 1 t − is approximated as a linear combination of pixels within a spatial neighborhood along the motion trajectory within the rebuilt frame 2 t − . Applying the least mean square algorithm, the coefficient of the first forward AR model is derived. Secondly, the pixels within the rebuilt frame 2 t − are approximated by the corresponding pixels within rebuilt frame 1 t − . And then the geometric symmetric property of the AR model is exploited to derive the coefficient of the second forward AR model. Finally, the side information is generated as the average of the interpolations obtained by the two forward AR interpolations. The experimental results have demonstrated that the proposed AR model can significantly improve the PSNR of the side information compared to existing motion extrapolation based approaches.