Pradeepa Yahampath

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An algorithm for designing linear prediction-based two-channel multiple-description predictive-vector quantizers (MD-PVQs) for packet-loss channels is presented. This algorithm iteratively improves the encoder partition, the set of multiple description codebooks, and the linear predictor for a given channel loss probability, based on a training set of(More)
The practical design of multiple description quantizers for diversity-based communication is investigated. A simulated annealing based method is proposed for obtaining the optimal index assignment for a multiple-description vector quantizer. This method can be used to design quantizers having an arbitrary number of descriptions with equal or unequal(More)
Finite state vector quantization over a noisy channel is studied. A major drawback of a finite-state decoder is its inability to track the encoder in the presence of channel noise. In order to over come this problem, we propose a non-tracking decoder which directly estimates the codevec-tors used by a finite-state encoder. The design of channel matched(More)
This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is used to exploit the correlation between sources. A training-based algorithm is proposed for jointly designing the predictors, binning functions,(More)