Optimized filtering and reconstruction in predictive quantization with losses
2004 International Conference on Image Processing, 2004. ICIP '04.
the Drediction i s desirable, let alone to immove upon the standard Consider a communication system in which a filtered and quantized signal i s sent over a channel with erasures and (potentially) additive noise. Linear MMSE estimation i s achieved in such a system by Kalman filtering. Allowing any Markov ensure process and any Markov-state jump linear signal generation modcl. i t is shown that the estimation performance at the receiver can be computed as a deterministic optimization with
... mization with linear matrix inequality (LMI) constraints rather than a pscudorandom simulmion. Furthermore. in contra1 to the case without erasures, the filtering in the transmitter should not necessdy bz MMSE prediction (whitening); a procedure i s givcn to find a locally optimal prefilter. The main tools are recent LMI characterizations of asymptotic state estimation error covariance and output estimation enor variance for discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. As anvther applicativn of this framework, a novel analysis and optimization of a "strenminz" venion of multiplc description coding based on subsampling is outlined.