Asymptotically optimal model estimation for quantization

Alexey Ozerov 1 W. Bastiaan Kleijn 2
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Using high-rate theory approximations we introduce flexible practical quantizers based on possibly non-Gaussian models in both the constrained resolution (CR) and the constrained entropy cases. We derive model estimation criteria optimizing asymptotic (with increasing rate) quantizer performance. We show that in the CR case the optimal criterion is different from the maximum likelihood criterion commonly used for that purpose and introduce a new criterion that we call constrained resolution minimum description length (CR-MDL). We apply these principles to the generalized Gaussian scaled mixture model, which is accurate for many real-world signals. We provide an explanation of the reason why the CR-MDL improves quantization performance in the CR case and show that CR-MDL can compensate for a possible mismatch between model and data distribution. Thus, this criterion is of a great interest for practical applications. Our experiments apply the new quantization method to controllable artificial data and to the commonly used modulated lapped transform representation of audio signals. We show that both the CR-MDL criterion and a non-Gaussian modeling have significant advantages.
Type de document :
Article dans une revue
IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2011, 59 (4), pp.1031-1042
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Soumis le : vendredi 7 janvier 2011 - 15:08:58
Dernière modification le : vendredi 16 novembre 2018 - 01:27:32
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Alexey Ozerov, W. Bastiaan Kleijn. Asymptotically optimal model estimation for quantization. IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2011, 59 (4), pp.1031-1042. 〈inria-00553532〉



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