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

Tecnun, University of Navarra, Paseo de Manuel Lardizábal 13, 20018 San Sebastián, Spain
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Entropy 2019, 21(10), 965; https://doi.org/10.3390/e21100965
Received: 4 August 2019 / Revised: 27 September 2019 / Accepted: 28 September 2019 / Published: 2 October 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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.
Keywords: source coding; rate distortion function (RDF); Gaussian vector; asymptotically wide sense stationary (AWSS) vector source; block discrete Fourier transform (DFT) source coding; rate distortion function (RDF); Gaussian vector; asymptotically wide sense stationary (AWSS) vector source; block discrete Fourier transform (DFT)
MDPI and ACS Style

Zárraga-Rodríguez, M.; Gutiérrez-Gutiérrez, J.; Insausti, X. A Low-Complexity and Asymptotically Optimal Coding Strategy for Gaussian Vector Sources. Entropy 2019, 21, 965.

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