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Proceedings Paper

Fast motion vector estimation with a Markov model for MPEG
Author(s): Sungook Kim; C.-C. Jay Kuo
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Paper Abstract

This paper presents a new approach for motion vector estimation. We first propose a stochastic model to describe the temporal correlation of motion vectors. We show that the optimal motion vectors can be obtained through maximum a posteriori sequence estimation method. This method is however not practically implementable due to its high computational complexity and many unknown modeling parameters. However, motivated by this theoretical framework, we propose a modified algorithm which is simple, accurate, and fast. First a set of good motion vector candidates is determined. By examining the distribution of these motion vector candidates, we can estimate the noise level as well as select the best motion vector by using the temporal correlations. Then, the next search window can be predicted by examining the trend of the motion vector variation and the noise level (a higher noise level leading to a large search window). In this way, we can reduce the search operation up to less than 0.5% compared to full block search. The excellent performance of the proposed algorithm is demonstrated through extensive experiments.

Paper Details

Date Published: 17 April 1995
PDF: 12 pages
Proc. SPIE 2419, Digital Video Compression: Algorithms and Technologies 1995, (17 April 1995); doi: 10.1117/12.206360
Show Author Affiliations
Sungook Kim, Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 2419:
Digital Video Compression: Algorithms and Technologies 1995
Arturo A. Rodriguez; Robert J. Safranek; Edward J. Delp, Editor(s)

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