A Low-Complexity and Asymptotically Optimal Coding Strategy for Gaussian Vector Sources

In this paper, we present a low-complexity coding strategy to encode (compress) finite-length data blocks of Gaussian vector sources. We show that for large enough data blocks of a Gaussian asymptotically wide sense stationary (AWSS) vector source, the rate of the coding strategy tends to the lowest...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 21; no. 10; p. 965
Main Authors Zárraga-Rodríguez, Marta, Gutiérrez-Gutiérrez, Jesús, Insausti, Xabier
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2019
MDPI
Subjects
Asymptotic properties
asymptotically wide sense stationary (awss) vector source
Autoregressive processes
block discrete fourier transform (dft)
Coding
Complexity
Correlation analysis
Fourier transforms
gaussian vector
Random variables
rate distortion function (rdf)
source coding
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Summary:In this paper, we present a low-complexity coding strategy to encode (compress) finite-length data blocks of Gaussian vector sources. We show that for large enough data blocks of a Gaussian asymptotically wide sense stationary (AWSS) vector source, the rate of the coding strategy tends to the lowest possible rate. Besides being a low-complexity strategy it does not require the knowledge of the correlation matrix of such data blocks. We also show that this coding strategy is appropriate to encode the most relevant Gaussian vector sources, namely, wide sense stationary (WSS), moving average (MA), autoregressive (AR), and ARMA vector sources.
ISSN:1099-4300
1099-4300
DOI:10.3390/e21100965