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Multiple description coding based on Gaussian mixture models
KTH, School of Electrical Engineering (EES), Sound and Image Processing. KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
KTH, School of Electrical Engineering (EES), Sound and Image Processing. KTH, School of Information and Communication Technology (ICT), Centres, Center for Wireless Systems, Wireless@kth.
2005 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 12, no 6, 449-452 p.Article in journal (Refereed) Published
Abstract [en]

An algorithm for multiple description coding (MDC) based on Gaussian mixture models (GMMs) is presented. Based on the parameters of the GMM, the algorithm combines MDC scalar quantizers, yielding a source-optimized vector MDC system. The performance is evaluated on a speech spectrum source in terms of mean-squared error and log spectral distortion. It is demonstrated experimentally that the proposed system outperforms single description coding and repetition coding over a wide range of channel failure probabilities. The proposed algorithm has a complexity that is linear in rate and dimension while retaining a near optimal vector quantizer point density.

Place, publisher, year, edition, pages
2005. Vol. 12, no 6, 449-452 p.
Keyword [en]
Gaussian mixture models (GMMs), joint source-channel coding, multiple description coding (MDC), quantization
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-38332DOI: 10.1109/LSP.2005.847887ISI: 000229157700005Scopus ID: 2-s2.0-20444368807OAI: oai:DiVA.org:kth-38332DiVA: diva2:436912
Note
QC 20110825Available from: 2011-08-25 Created: 2011-08-24 Last updated: 2017-12-08Bibliographically approved

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