A simple reversedcomplexity wynerziv video coding mode based on a spatial reduction framework A spatialresolution reduction based framework for incorporation of a WynerZiv frame coding mode in existing video codecs is presented, to enable a mode of operation with low encoding complexity. The core WynerZiv frame coder works on the Laplacian residual of a lowerresolution frame encoded by a regular codec at reduced resolution. The quantized transform coefficients of the residual frame are mapped to cosets to reduce the bitrate. A detailed ratedistortion analysis and procedure for obtaining the optimal parameters based on a realistic statistical model for the transform coefficients and the side information is also presented. The decoder iteratively conducts motionbased sideinformation generation and coset decoding, to gradually refine the estimate of the frame. Preliminary results are presented for application to the H.263 video codec.

Reconstruction for Distributed Video Coding: A Markov Random Field Approach with ContextAdaptive Smoothness Prior An important issue in WynerZiv video coding is the reconstruction of WynerZiv frames with decoded bitplanes. So far, there are two major approaches: the Maximum a Posteriori (MAP) reconstruction and the Minimum Mean Square Error (MMSE) reconstruction algorithms. However, these approaches do not exploit smoothness constraints in natural images. In this paper, we model a WynerZiv frame by Markov random fields (MRFs), and produce reconstruction results by finding an MAP estimation of the MRF model. In the MRF model, the energy function consists of two terms: a data term, MSE distortion metric in this paper, measuring the statistical correlation between sideinformation and the source, and a smoothness term enforcing spatial coherence. In order to better describe the spatial constraints of images, we propose a contextadaptive smoothness term by analyzing the correspondence between the output of SlepianWolf decoding and successive frames available at decoders. The significance of the smoothness term varies in accordance with the spatial variation within different regions. To some extent, the proposed approach is an extension to the MAP and MMSE approaches by exploiting the intrinsic smoothness characteristic of natural images. Experimental results demonstrate a considerable performance gain compared with the MAP and MMSE approaches.