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Empirical quantizer design in the presence of source noise or channel noise

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3 Author(s)
Linder, T. ; Dept. of Math. & Comput. Sci., Tech. Univ. Budapest, Hungary ; Lugosi, G. ; Zeger, K.

The problem of vector quantizer empirical design for noisy channels or for noisy sources is studied. It is shown that the average squared distortion of a vector quantizer designed optimally from observing clean independent and identically distributed (i.i.d.) training vectors converges in expectation, as the training set size grows, to the minimum possible mean-squared error obtainable for quantizing the clean source and transmitting across a discrete memoryless noisy channel. Similarly, it is shown that if the source is corrupted by additive noise, then the average squared distortion of a vector quantizer designed optimally from observing i.i.d. noisy training vectors converges in expectation, as the training set size grows, to the minimum possible mean-squared error obtainable for quantizing the noisy source and transmitting across a noiseless channel. Rates of convergence are also provided

Published in:

Information Theory, IEEE Transactions on  (Volume:43 ,  Issue: 2 )