Bayesian resolution-enhancement framework for transform-coded video

Resolution enhancement for video sequences has always been an attractive application in multimedia signal processing. “Superresolution” methods, that combine non-redundant information from a set of low-resolution images, are beginning to be applied to the most popular video compression standard, MPEG. Bayesian approaches, which are very successful for raw video, largely fail for MPEG video, since they do not incorporate the compression process into their models. This compression process introduces quantization noise, which is comparable to the additive noise that is used in the Bayesian models. In this paper we present an analytical derivation that combines the quantization and additive noises in a stochastic framework for MPEG-compressed video. This is a general framework in the sense that different video acquisition models, source statistics, implementation techniques can be used with it.