Asymptotically Optimal Model Estimation for Quantization
Autor: | W B Kleijn, A Ozerov |
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Rok vydání: | 2011 |
Předmět: |
Mathematical optimization
Optimal estimation Quantization (signal processing) Gaussian 020206 networking & telecommunications 02 engineering and technology Mixture model Mixture theory 030507 speech-language pathology & audiology 03 medical and health sciences symbols.namesake Asymptotically optimal algorithm 0202 electrical engineering electronic engineering information engineering symbols Electrical and Electronic Engineering 0305 other medical science Minimum description length Gaussian process Algorithm Mathematics |
Zdroj: | IEEE Transactions on Communications. 59:1031-1042 |
ISSN: | 0090-6778 |
Popis: | 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. |
Databáze: | OpenAIRE |
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