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Summary: BAYESIAN RESOLUTION-ENHANCEMENT FRAMEWORK FOR
TRANSFORM-CODED VIDEO
Bahadir K. Gunturk, Yucel Altunbasak, and Russell Mersereau
Center for Signal and Image Processing
Georgia Institute of Technology
Atlanta, GA 30332-0250
ABSTRACT
Resolution enhancement for video sequences has always been an
attractive application in multimedia signal processing. "Superres-
olution" 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 ap-
proaches, which are very successful for raw video, largely fail for
MPEG video, since they do not incorporate the compression pro-
cess into their models. This compression process introduces quan-
tization noise, which is comparable to the additive noise that is
used in the Bayesian models. In this paper we present an analyt-
ical 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
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