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Modeling Quantized Coefficients with Generalized Gaussian Distribution with Exponent 1 / m, \(m=2,3,\ldots \)

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Man-Machine Interactions 5 (ICMMI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 659))

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Abstract

The different types of signals in the image and signal processing applications can be modeled with generalized Gaussian distribution (GGD). When limiting to the special cases, then the closed form equations can be determined. The special cases with the exponents \(p=2\) (Gaussian distribution), \(p=1\) (Laplacian distribution), \(p=1/2\) and \(p=1/3\) are considered in literature. In the article, more general approach for the exponents 1 / m, \(m=2,3,\ldots \) is analyzed, which are related to the peaky shapes of GGD. The maximum likelihood method for a discrete random variable is derived for this subclass of distributions.

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Authors and Affiliations

  1. Chair of Signal Processing and Multimedia Engineering, West-Pomeranian University of Technology in Szczecin, ul. 26-Kwietnia 10, 71-126, Szczecin, Poland

    Robert Krupiński

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  1. Robert Krupiński
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Correspondence to Robert Krupiński .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harezlak

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

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Krupiński, R. (2018). Modeling Quantized Coefficients with Generalized Gaussian Distribution with Exponent 1 / m, \(m=2,3,\ldots \) . In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-67792-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67791-0

  • Online ISBN: 978-3-319-67792-7

  • eBook Packages: EngineeringEngineering (R0)

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