Hierarchical stochastic fast search motion estimation algorithm

Many fast search motion estimation algorithms have been developed to reduce the computational cost required by full-search algorithms. Fast search motion estimation techniques often converge to a local minimum, providing a significant reduction in computational cost. The motion vector measurement pr...

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Veröffentlicht in:IET computer vision Jg. 6; H. 1; S. 21 - 28
Hauptverfasser: Tedmori, S., Al-Najdawi, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage Institution of Engineering and Technology 01.01.2012
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Applied sciences
Blocking
Computational efficiency
Detection, estimation, filtering, equalization, prediction
Estimates
Exact sciences and technology
Image processing
Information, signal and communications theory
Mathematical analysis
Motion simulation
Searching
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Vectors (mathematics)
Performance evaluation
Motion estimation
Image processing
Kalman filter
Stochastic method
Computational complexity
Search algorithm
Block matching
Accuracy
Displacement measurement
Signal processing
Kalman filtering
Fast algorithm
ISSN:1751-9632, 1751-9640
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Zusammenfassung:Many fast search motion estimation algorithms have been developed to reduce the computational cost required by full-search algorithms. Fast search motion estimation techniques often converge to a local minimum, providing a significant reduction in computational cost. The motion vector measurement process in fast search algorithms is subject to noise and matching errors. Therefore researchers have investigated the use of Kalman filtering in order to seek optimal estimates. In this work, the authors propose a new fast stochastic motion estimation technique that requires 5% of the total computations required by the full-search algorithm, and results in a quality that outperforms most of the well-known fast searching algorithms. The measured motion vectors are obtained using a simplified hierarchical search block-matching algorithm, and are used as the measurement part of the Kalman filter. As for the prediction part of the filter, it is assumed that the motion vector of a current block can be predicted from its four neighbouring blocks. Using the predicted and measured motion vectors, the best estimates for motion vectors are obtained. Using standard methods of accuracy measurements, results show that the performance of the proposed technique approaches that of the frill-search algorithm.
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ISSN:1751-9632
1751-9640
DOI:10.1049/iet-cvi.2010.0188