Robust vector quantization for channels with memory

This study focuses on two issues: parametric modeling of the channel and index assignment of codevectors, to design a vector quantizer that achieves high robustness against channel errors. We first formulated the design of a robust zero-redundancy vector quantizer as a combinatorial optimization problem leading to a genetic search for the minimum distortion index assignment. This study also presents an index assignment algorithm based on Gilbert's model with parameter values estimated using a real-coded genetic algorithm. Simulation results indicate that the global explorative properties of genetic algorithms make them very effective at estimating Gilbert's model parameters and by using this model the index assignment can be developed to respond to channel conditions.