Restoration of compressed video using temporal information This paper proposes a Bayesian method for the restoration of video sequences compressed using the discrete cosine transform (DCT). Two elements, both part of the Bayesian observation model, distinguish the proposed algorithm from the majority of other methods in the literature. The proposed algorithm incorporates temporal information from nearby framespast, present and futurewhen forming an estimate of the current frame. Furthermore, this work uses a spatiallyvarying noise model to account for the noise introduced by quantization of the DCT coefficients. These two aspects of the observation model are used in conjunction with a HuberMarkov Random Field (HMRF) model to form a Bayesian estimate of each frame in the compressed video sequence.

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.