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Optimal parameter estimation for model-based quantization
KTH, School of Electrical Engineering (EES), Sound and Image Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2009 (English)In: 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2009, 2497-2500 p.Conference paper (Refereed)
Abstract [en]

We address optimal model estimation for model-based vector quantization for both the constrained resolution (CR) and constrained entropy (CE) cases. To this purpose we derive under high-rate (HR) theory assumptions the rate-distortion (RD) relations for these two quantization scenarios assuming a Gaussian model. Based on the RD relations we show that the maximum likelihood (ML) criterion leads to optimal performance for CE quantization, but not for CR quantization. We introduce a new model estimation criterion for CR quantization that is optimal (under HR theory assumptions) in terms of the RD relation. Our experiments confirm that the proposed criterion for model identification outperforms the ML criterion for a range of conditions.

Place, publisher, year, edition, pages
2009. 2497-2500 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword [en]
Constrained resolution, model-based quantization, model estimation, rate-distortion relation, high-rate theory
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-36105DOI: 10.1109/ICASSP.2009.4960129ISI: 000268919201164ScopusID: 2-s2.0-70349210170OAI: diva2:430387
IEEE International Conference on Acoustics, Speech and Signal Processing Taipei, TAIWAN, APR 19-24, 2009
QC 20110708Available from: 2011-07-08 Created: 2011-07-08 Last updated: 2011-07-08Bibliographically approved

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Kleijn, W. Bastiaan
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Sound and Image ProcessingACCESS Linnaeus Centre
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