Optimal parameter estimation for model-based quantization
2009 (English)In: 2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2009, 2497-2500 p.Conference paper (Refereed)
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
Constrained resolution, model-based quantization, model estimation, rate-distortion relation, high-rate theory
IdentifiersURN: urn:nbn:se:kth:diva-36105DOI: 10.1109/ICASSP.2009.4960129ISI: 000268919201164ScopusID: 2-s2.0-70349210170OAI: oai:DiVA.org:kth-36105DiVA: diva2:430387
IEEE International Conference on Acoustics, Speech and Signal Processing Taipei, TAIWAN, APR 19-24, 2009
QC 201107082011-07-082011-07-082011-07-08Bibliographically approved