  This article proposes a novel iterative algorithm based on Low Density Parity
Check (LDPC) codes for compression of correlated sources at rates approaching
the Slepian-Wolf bound. The setup considered in the article looks at the
problem of compressing one source at a rate determined based on the knowledge
of the mean source correlation at the encoder, and employing the other
correlated source as side information at the decoder which decompresses the
first source based on the estimates of the actual correlation. We demonstrate
that depending on the extent of the actual source correlation estimated through
an iterative paradigm, significant compression can be obtained relative to the
case the decoder does not use the implicit knowledge of the existence of
correlation.
