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Run-length compression of quantized Gaussian stationary signals
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2012 (English)In: Random Operators and Stochastic Equations, ISSN 0926-6364, E-ISSN 1569-397X, Vol. 20, no 4, 311-328 p.Article in journal (Refereed) Published
Abstract [en]

We consider quantization of random continuous-valued signals. In practice, analogue signals are quantized at sampling points with further compression. We study probabilistic models for run-length encoding (RLE) algorithm applied to quantized sampled random signals (Gaussian processes). This compression technique is widely used in digital signal and image processing. The mean (inverse) RLE compression ratio (or data rate savings) and its statistical inference are considered. In particular, the asymptotic normality for some estimators of this characteristic is shown. Numerical experiments for synthetic and real data are presented.

Place, publisher, year, edition, pages
2012. Vol. 20, no 4, 311-328 p.
Keyword [en]
uniform quantization, run-length encoding, Gaussian process, Asymptotic normality, Gaussian process, compression ratio, run-length encoding, uniform quantization
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-61620DOI: 10.1515/rose-2012-0015OAI: oai:DiVA.org:umu-61620DiVA: diva2:570806
Available from: 2012-11-20 Created: 2012-11-20 Last updated: 2017-12-07Bibliographically approved

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Seleznjev, OlegShykula, Mykola
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