Markov model aided decoding for image transmission using soft-decision-feedback

Soft-decision-feedback MAP decoders are developed for joint source/channel decoding (JSCD) which uses the residual redundancy in two-dimensional sourc es. The source redundancy is described by a second order Markov model which is made available to the receiver for row-by-row decoding, wherein the out put for one row is used to aid the decoding of the next row Performance can be improved by generalizing so as to increase the vertical depth of the de coder. This is called sheet decoding, and entails generalizing trellis deco ding of one-dimensional data to trellis decoding of two-dimensional data (2 -D), The proposed soft-decision-feedback sheet decoder is based on the Bahl algorithm, and it is compared to a hard-decision-feedback sheet decoder wh ich is based on the Viterbi algorithm. The method is applied to 3-bit DPCM picture transmission over a binary symm etric channel, and it is found that the soft decision-feedback decoder with vertical depth V performs approximately as well as the hard-decision-feedb ack decoder with vertical depth V + 1, Because the computational requiremen t of the decoders depends exponentially on the vertical depth, the soft-dec ision-feedback decoder offers significant reduction in complexity. For stan dard monochrome Lena, at a channel bit error rate of 0.05, the V = 1 and V = 2 soft-decision-feedback decoder JSCD gains in RSNR are 5.0 and 6.3 dB, r espectively.