Quantization of Random Processes and Related Statistical Problems

In this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memor...

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Main Author: Shykula, Mykola
Format: Doctoral Thesis
Language:English
Published: Umeå universitet, Matematik och matematisk statistik 2006
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-883
http://nbn-resolving.de/urn:isbn:91-7264-183-5
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spelling ndltd-UPSALLA1-oai-DiVA.org-umu-8832013-01-08T13:04:20ZQuantization of Random Processes and Related Statistical ProblemsengShykula, MykolaUmeå universitet, Matematik och matematisk statistikUmeå : Matematik och matematisk statistik2006scalar quantizationrandom processratedistortionadditive noise modelrun-length encodingcompressionsample estimateasymptotical normalityMathematical statisticsMatematisk statistikIn this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memory capacity (or quantization rate) needed for quantized process realizations by exploiting the correlation structure of the model random process. The thesis consists of an introductory survey of the subject and related theory followed by four included papers (A-D). In Paper A we develop a quantization coding method when quantization levels crossings by a process realization are used for its coding. Asymptotical behavior of mean quantization rate is investigated in terms of the correlation structure of the original process. For uniform and non-uniform quantization, we assume that the quantization cellwidth tends to zero and the number of quantization levels tends to infinity, respectively. In Papers B and C we focus on an additive noise model for a quantized random process. Stochastic structures of asymptotic quantization errors are derived for some bounded and unbounded non-uniform quantizers when the number of quantization levels tends to infinity. The obtained results can be applied, for instance, to some optimization design problems for quantization levels. Random signals are quantized at sampling points with further compression. In Paper D the concern is statistical inference for run-length encoding (RLE) method, one of the compression techniques, applied to quantized stationary Gaussian sequences. This compression method is widely used, for instance, in digital signal and image processing. First, we deal with mean RLE quantization rates for various probabilistic models. For a time series with unknown stochastic structure, we investigate asymptotic properties (e.g., asymptotic normality) of two estimates for the mean RLE quantization rate based on an observed sample when the sample size tends to infinity. These results can be used in communication theory, signal processing, coding, and compression applications. Some examples and numerical experiments demonstrating applications of the obtained results for synthetic and real data are presented. Doctoral thesis, comprehensive summaryinfo:eu-repo/semantics/doctoralThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-883urn:isbn:91-7264-183-5application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic scalar quantization
random process
rate
distortion
additive noise model
run-length encoding
compression
sample estimate
asymptotical normality
Mathematical statistics
Matematisk statistik
spellingShingle scalar quantization
random process
rate
distortion
additive noise model
run-length encoding
compression
sample estimate
asymptotical normality
Mathematical statistics
Matematisk statistik
Shykula, Mykola
Quantization of Random Processes and Related Statistical Problems
description In this thesis we study a scalar uniform and non-uniform quantization of random processes (or signals) in average case setting. Quantization (or discretization) of a signal is a standard task in all nalog/digital devices (e.g., digital recorders, remote sensors etc.). We evaluate the necessary memory capacity (or quantization rate) needed for quantized process realizations by exploiting the correlation structure of the model random process. The thesis consists of an introductory survey of the subject and related theory followed by four included papers (A-D). In Paper A we develop a quantization coding method when quantization levels crossings by a process realization are used for its coding. Asymptotical behavior of mean quantization rate is investigated in terms of the correlation structure of the original process. For uniform and non-uniform quantization, we assume that the quantization cellwidth tends to zero and the number of quantization levels tends to infinity, respectively. In Papers B and C we focus on an additive noise model for a quantized random process. Stochastic structures of asymptotic quantization errors are derived for some bounded and unbounded non-uniform quantizers when the number of quantization levels tends to infinity. The obtained results can be applied, for instance, to some optimization design problems for quantization levels. Random signals are quantized at sampling points with further compression. In Paper D the concern is statistical inference for run-length encoding (RLE) method, one of the compression techniques, applied to quantized stationary Gaussian sequences. This compression method is widely used, for instance, in digital signal and image processing. First, we deal with mean RLE quantization rates for various probabilistic models. For a time series with unknown stochastic structure, we investigate asymptotic properties (e.g., asymptotic normality) of two estimates for the mean RLE quantization rate based on an observed sample when the sample size tends to infinity. These results can be used in communication theory, signal processing, coding, and compression applications. Some examples and numerical experiments demonstrating applications of the obtained results for synthetic and real data are presented.
author Shykula, Mykola
author_facet Shykula, Mykola
author_sort Shykula, Mykola
title Quantization of Random Processes and Related Statistical Problems
title_short Quantization of Random Processes and Related Statistical Problems
title_full Quantization of Random Processes and Related Statistical Problems
title_fullStr Quantization of Random Processes and Related Statistical Problems
title_full_unstemmed Quantization of Random Processes and Related Statistical Problems
title_sort quantization of random processes and related statistical problems
publisher Umeå universitet, Matematik och matematisk statistik
publishDate 2006
url http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-883
http://nbn-resolving.de/urn:isbn:91-7264-183-5
work_keys_str_mv AT shykulamykola quantizationofrandomprocessesandrelatedstatisticalproblems
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