  This work is devoted to practical joint source channel coding. Although the
proposed approach has more general scope, for the sake of clarity we focus on a
specific application example, namely, the transmission of digital images over
noisy binary-input output-symmetric channels. The basic building blocks of most
state-of the art source coders are: 1) a linear transformation; 2) scalar
quantization of the transform coefficients; 3) probability modeling of the
sequence of quantization indices; 4) an entropy coding stage. We identify the
weakness of the conventional separated source-channel coding approach in the
catastrophic behavior of the entropy coding stage. Hence, we replace this stage
with linear coding, that maps directly the sequence of redundant quantizer
output symbols into a channel codeword. We show that this approach does not
entail any loss of optimality in the asymptotic regime of large block length.
However, in the practical regime of finite block length and low decoding
complexity our approach yields very significant improvements. Furthermore, our
scheme allows to retain the transform, quantization and probability modeling of
current state-of the art source coders, that are carefully matched to the
features of specific classes of sources. In our working example, we make use of
``bit-planes'' and ``contexts'' model defined by the JPEG2000 standard and we
re-interpret the underlying probability model as a sequence of conditionally
Markov sources. The Markov structure allows to derive a simple successive
coding and decoding scheme, where the latter is based on iterative Belief
Propagation. We provide a construction example of the proposed scheme based on
punctured Turbo Codes and we demonstrate the gain over a conventional separated
scheme by running extensive numerical experiments on test images.
