Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method

This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2010-01, Vol.2010 (1), p.210937-210937
Main Authors: Boudlal, Abdenaceur, Nsiri, Benayad, Aboutajdine, Driss
Format: Article
Language:English
Subjects:
Noise
Parameter estimation
Soccer
Standard deviation
Statistical analysis
Statistical methods
Studies
Video compression
Online Access:Get full text
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Summary:This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.
ISSN:1687-6172
1687-6180
1687-6180
DOI:10.1186/1687-6180-2010-210937