Side information generation with auto regressive model for low-delay distributed video coding

In this paper, we propose an auto regressive (AR) model to generate the high quality side information (SI) for Wyner-Ziv (WZ) frames in low-delay distributed video coding, where the future frames are not used for generating SI. In the proposed AR model, the SI of each pixel within the current WZ frame t is generated as a linear weighted summation of the pixels within a window in the previous reconstructed WZ/Key frame t - 1 along the motion trajectory. To obtain accurate SI, the AR model is used in both temporal directions in the reconstructed WZ/Key frames t - 1 and t - 2, and then the regression results are fused with traditional extrapolation result based on a probability model. In each temporal direction, a weighting coefficient set is computed by the least mean square method for each block in the current WZ frame t. In particular, due to the unavailability of future frames in low-delay distributed video coding, a centrosymmetric rearrangement is proposed for pixel generation in the backward direction. Various experimental results demonstrate that the proposed model is able to achieve a higher performance compared to the existing SI generation methods.