The output of a discrete Markov source is to be encoded instantaneously by a variable-rate encoder and decoded by a finite-state decoder. Our performance measure is a linear combination of the distortion and the insta...
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The output of a discrete Markov source is to be encoded instantaneously by a variable-rate encoder and decoded by a finite-state decoder. Our performance measure is a linear combination of the distortion and the instantaneous rate. Structure theorems, pertaining to the encoder and next-state functions, are derived for every given finite-state decoder, which can have access to side information.
In this paper, finite-state vector quantization (FSVQ) over noisy channels is studied. In particular a robust, time-recursive algorithm is proposed for reconstructing the output from a finite-state encoder, observed t...
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ISBN:
(纸本)0780374029
In this paper, finite-state vector quantization (FSVQ) over noisy channels is studied. In particular a robust, time-recursive algorithm is proposed for reconstructing the output from a finite-state encoder, observed through a noisy channel. In contrast to an ordinary finite-state decoder, the proposed decoder exhibits graceful degradation of performance with increasing channel noise. We also consider the iterative optimization of encoder and decoder for designing channel optimized FSVQ. Simulation results based on Gauss-Markov source and additive white Gaussian noise channel are presented, and it is shown that robust FSVQ designed by methodology introduced here can outperform memoryless channel optimized vector quantization at the same rate. Soft-decoding at the receiver, which provides an additional performance gain, is also considered.
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