Progressive transmission of images using MAP detection over channels with memory
We propose a new maximum a posteriori (MAP) detector, without the need for explicit channel coding, to lessen the impact of communication channel errors an compressed image sources, The MAP detector exploits the spatial correlation in the compressed bitstream as well as the temporal memory in the channel to correct channel errors, We first present a technique for computing the residual redundancy inherent in a Compressed grayscale image (compressed using VQ). The performance of the proposed MAP detector is compared to that of: a memoryless MAP detector. We also investigate the dependence of the performance on memory characteristics of the Gilbert-Elliott channel as well as average Channel error rate. Finally, we study the robustness of the proposed MAP detector's performance to estimation errors.
map detection channels with memory residual redundancy non-causal source modeling vector quantization viterbi algorithm