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On Gibbs-Markov Models for Motion Computation

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Video Data Compression for Multimedia Computing

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 378))

Abstract

This chapter deals with the issues of Gibbs-Markov modeling of various phenomena associated with the computation of motion. The envisaged applications are in video compression at various bit rates, video processing and image analysis. First, Gibbs-Markov models that define the relationship between images and motion descriptors is discussed. These models are usually incorporated into the so-called “likelihood” term in the Bayesian estimation. Then, we proceed to the discussion of “prior” models for various motion descriptors, such as velocity, acceleration, motion discontinuities (or segmentation), occlusions. This part also includes the models for the interaction of various descriptors. Secondly, we discuss the cost function resulting from the above models by using well known estimation criteria, such as the Maximum A Posteriori Probability (MAP) criterion. Finally, we deal with the issue of optimizing the result cost function. We discuss the multi-resolution/multi-scale and deterministic/stochastic approaches to such an optimization.

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Authors and Affiliations

  1. INRS-Télécommunications, Verdun, Québec, H3E 1H6, Canada

    J. Konrad & C. Stiller

  2. Robert Bosch GmbH, D-31132, Hildesheim, Germany

    C. Stiller

Authors
  1. J. Konrad
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  2. C. Stiller
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Editor information

Editors and Affiliations

  1. Texas Tech University, USA

    Hua Harry Li  & Shan Sun  & 

  2. University of Massachusetts, USA

    Haluk Derin

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© 1997 Springer Science+Business Media New York

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Konrad, J., Stiller, C. (1997). On Gibbs-Markov Models for Motion Computation. In: Li, H.H., Sun, S., Derin, H. (eds) Video Data Compression for Multimedia Computing. The Springer International Series in Engineering and Computer Science, vol 378. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6239-9_4

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  • DOI: https://doi.org/10.1007/978-1-4615-6239-9_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7862-4

  • Online ISBN: 978-1-4615-6239-9

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