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Proceedings Paper

Time-space segmentation and motion estimation based on higher order statistics
Author(s): Alessandro Neri; Giuseppe Russo; Stefania Colonnese; Paolo Talone
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Paper Abstract

The paper illustrates a comprehensive method for the motion compensation to be used in predictive video coding. The method is based on the observation that structured artifacts as those consisting of isolated points, lines, edges, organized textures are directly perceived by the user, while artifacts resembling realizations of gaussian processes can be considered less important. A fidelity criterion based on the Mean Forth-Cumulant as indirect estimate of the local entropy level is then applied to drive both the segmentation and the motion estimation phases. The motion estimator is conceptually similar to the higher order moments techniques employed in time delay estimation, and takes advantage of the Gaussian signals rejection capability, typical of the higher order cumulants. The contribution describes the theoretical framework of cumulant based motion estimation. The performance of a coder based on the discrimination of the temporal activity by means of cumulants, is illustrated through experimental data.

Paper Details

Date Published: 28 October 1994
PDF: 12 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190882
Show Author Affiliations
Alessandro Neri, Univ. di Roma III (Italy)
Giuseppe Russo, Fondazione Ugo Bordoni (Italy)
Stefania Colonnese, Fondazione Ugo Bordoni (Italy)
Paolo Talone, Fondazione Ugo Bordoni (Italy)


Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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