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

Zárraga-Rodríguez, M. (Marta de); Gutiérrez-Gutiérrez, J. (Jesús); Insausti-Sarasola, X. (Xabier)

Autor(es):

Zárraga-Rodríguez, M. (Marta de)

Gutiérrez-Gutiérrez, J. (Jesús)

Insausti-Sarasola, X. (Xabier)

Palabras clave :

Materias Investigacion::Ingeniería::Generalidades
Source coding
Rate distortion function (RDF)
Gaussian vector
Asymptotically wide sense stationary (AWSS) vector source
Block discrete Fourier transform (DFT)

Fecha de publicación :

2019

Editorial :

MDPI AG

ISSN :

1099-4300

Nota:

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/).

Cita:

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

Resumen

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.

URI :

https://hdl.handle.net/10171/61894

DOI:

10.3390/e21100965

Aparece en las colecciones:

DA - TECNUN - Ingeniería Biomédica y Ciencias - Artículos de revista

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