An auto-regressive model for checkboard splitting based Wyner-Ziv coding

An auto-regressive (AR) based side information (SI) generation is proposed in this paper for block based chessboard pattern Wyner-Ziv (WZ) coding, where each WZ frame is split into two sets at encoder and then encoded separately. At the decoder, one set of the WZ frame will be firstly reconstructed, and then proposed AR model is used to generate the SI of the other set, where each pixel is generated as a linear weighted summation of pixels within two square windows in the previous and following reconstructed WZ/key frames along the motion trajectory. To obtain high quality SI for the second set, reconstructed pixels in the four neighboring blocks of the first set are employed to derive accurate AR coefficients. Several experimental results demonstrate that the proposed AR model is able to improve the quality of the SI for the second set of the WZ frame, which leads to the improvement of the rate-distortion performance of the WZ coding.