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 sources. 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 output 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 decoder. This is called sheet decoding, and entails generalizing trellis decoding 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 which is based on the Viterbi algorithm. The method is applied to 3-bit DPCM picture transmission over a binary symmetric channel, and it is found that the soft-decision-feedback decoder with vertical depth V performs approximately as well as the hard-decision-feedback decoder with vertical depth V+1. Because the computational requirement of the decoders depends exponentially on the vertical depth, the soft-decision-feedbark decoder offers significant reduction in complexity. For standard 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, respectively