Swarm intelligence
Flexible triangle search algorithm for block-based motion estimation
EURASIP Journal on Applied Signal Processing
Block Matching Algorithm Based on Particle Swarm Optimization for Motion Estimation
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
Modeling of pattern-based block motion estimation and its application
IEEE Transactions on Circuits and Systems for Video Technology
A search patterns switching algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
An Efficient Bidirectional Frame Prediction Using Particle Swarm Optimization Technique
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
Adaptive rood pattern search for fast block-matching motion estimation
IEEE Transactions on Image Processing
A novel four-step search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
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To improve the performance of motion estimation in video coding, we propose a novel block matching algorithm, which utilize the global search ability of particle swarm optimization (PSO) with mutation operator and the local search ability of simplex method (SM). According to the center-biased and temporal-spatial correlation feature of motion vector and the global randomness of PSO, the fixed and random points are selected as the initial individual. Then, block matching process is executed by the updating of the position and velocity of individual. In order to accelerate the convergence of PSO and improve the accuracy of local search, mutation operator and simplex method are used. Meanwhile, based on the feature of static macro blocks, the proposed algorithm intelligently uses early termination strategies. Experimental results demonstrate that the proposed algorithm has better PSNR values than conventional fast block matching algorithms especially for video sequences with violent motion while the computational complexity of the proposed algorithm has negligible increase.