Single-Dimension Perturbation Glowworm Swarm Optimization Algorithm for Block Motion Estimation

In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on sing...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 10
Main Authors Liu, Xiangpin, Liu, Feng, Xuan, Shibin
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
Hindawi Limited
Subjects
Algorithms
Coding standards
Complexity
Design engineering
Genetic algorithms
Information technology
International conferences
Local optimization
Mathematical problems
Methods
Motion simulation
Optimization algorithms
Perturbation methods
Robotics
Searching
Software
Swarm intelligence
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Summary:In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on single-dimension perturbation glowworm swarm optimization algorithm is proposed. Single-dimension perturbation is a local search strategy which can improve the ability of local optimization. The proposed method not only has overcome the defect of falling into local optimum easily by taking use of both the global search ability of glowworm swarm optimization algorithm and the local optimization ability of single-dimension perturbation but also has reduced the computation complexity by using motion vector predictor and terminating strategies in view of the characteristic of video images. The experimental results show that the performance of the proposed method is better than that of other motion estimation methods for most video sequences, specifically for those video sequences with violent motion, and the searching precision has been improved obviously. Although the computational complexity of the proposed method is slightly higher than that of the classical methods, it is still far lower than that of full search method.
ISSN:1024-123X
1563-5147
DOI:10.1155/2013/610230