Efficient decoding of correlated sources with application to DPCM image coding

Three methods are proposed in this paper, in which the encoded signal is modelled as a Markov sequence. First, an optimum method for decoding correlated sequences is derived and it is shown to require Viterbi decoding. Then, a modified MAP method (MMAP) for Markov sequences is described. A maximal signal-to-noise (MSNR) receiver for DPCM systems is also developed that minimizes the distortion power due to channel errors; the appropriate cost matrix for this receiver is computed. These methods are applied to DPCM picture transmission over noisy channels and are compared to a recent method. The SNR graphs and the subjective examination of the enhanced pictures demonstrate that the proposed procedures are quite effective and are superior to a recent method. The MSNR receiver was found to be somewhat more effective than the MMAP receiver for a given order of the Markov source. The Markov modelling of the encoded signal is found to improve the SNR of the received pictures. Further more, it is observed that the gain increases with the increase in the order of the Markov model.

Efficient decoding of correlated sources with application to DPCM image coding Conference Paper