A Low-Complexity and Asymptotically Optimal Coding Strategy for Gaussian Vector Sources
Keywords: 
Materias Investigacion::Ingeniería::Generalidades
Source coding
Rate distortion function (RDF)
Gaussian vector
Asymptotically wide sense stationary (AWSS) vector source
Block discrete Fourier transform (DFT)
Issue Date: 
2019
Publisher: 
MDPI AG
Publisher Version: 
ISSN: 
1099-4300
Note: 
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Citation: 
Zárraga-Rodríguez, M. (Marta); Gutiérrez-Gutiérrez, J. (Jesús); Insausti-Sarasola, X.(Xabier). "A Low-Complexity and Asymptotically Optimal Coding Strategy for Gaussian Vector Sources". Entropy. 21(10) (965), 2019, 1 - 22
Abstract
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.

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