Asymptotically Optimal Model Estimation for Quantization

—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 a...

• Type: Journal
• Year: 2011
• Where: TCOM
• Authors: Alexey Ozerov, W. Bastiaan Kleijn
• Keywords: Constrained Entropy Cases, CR Case, Maximum Likelihood Criterion, TCOM 2011