Error concealment for video transmission with dual multiscale markov random field modeling

Abstract

A novel error concealment algorithm based on a stochastic modeling approach is proposed as a post-processing tool at the decoder side for recovering the lost information incurred during the transmission of encoded digital video bitstreams. In our proposed scheme, both the spatial and the temporal contextual features in video signals are separately modeled using the multiscale Markov random field (MMRF). The lost information is then estimated using maximum a posteriori (MAP) probabilistic approach based on the spatial and temporal MMRF models; hence, a unified MMRF-MAP framework. To preserve the high frequency information (in particular, the edges) of the damaged video frames through iterative optimization, a new adaptive potential function is also introduced in this paper. Comparing to the existing MRF-based schemes and other traditional concealment algorithms, the proposed dual MMRF (DMMRF) modeling method offers significant improvement on both objective peak signal-to-noise ratio (PSNR) measurement and subjective visual quality of restored video sequence.