Motion Estimation Based On Swarm Intelligence Algorithm

With the rapid development of science and the increasing demand of people,more and more attention has been paid to the research of video processing technology.Motion estimation is an important part in the process of video compression and coding,which can effectively reduce the redundancy between adjacent frames in video sequences.However,the computation of motion estimation is huge,which seriously affects the overall performance of the coding system.Therefore,motion estimation has always been the focus and hot spot of video processing technology.In the last ten years,the swarm intelligent optimization algorithm has attracted more and more attention because of its superior performance,and opened up a new way for the research of motion estimation.In order to obtain better performance of block matching motion estimation,two superior swarm intelligent optimization algorithms,particle swarm optimization and ant colony optimization,are studied in this paper.Then two motion estimation algorithms based on group intelligence optimization are proposed.The experimental results show that the performance of the two algorithms are better than that of the typical fast motion estimation algorithm.The performance of motion estimation algorithm based on improved ant colony optimization is superior.The main contents and innovations of this paper are as follows:1.In this paper,the motion estimation technology is deeply studied,and several typical block matching motion estimation algorithms are analyzed and summarized.2.A motion estimation algorithm based on hybrid particle swarm optimization is proposed.The algorithm not preserves the random search performance of the system,but designs the initial search population reasonably according to the distribution characteristics of the motion vectors.Then the chaotic differential evolution search is used to help the particle swarm optimization algorithm to do iteration,in which the chaotic sequence is used to optimize the mutation operator.The computational complexity of the algorithm is reduced effectively by the same point check scheme and appropriate termination strategy.The experimental results show that the algorithm achieves a dynamic equilibrium in the search accuracy and search speed,and its overall performance is better than that of the typical fast motion estimation algorithm.3.A motion estimation algorithm based on improved ant colony optimization is proposed.According to the characteristics of ant colony optimization algorithm,a unique search pattern and initial search point group are designed for ant colony.The algorithm isbased on the improved probability transfer formula for iterative optimization.The search ability of the algorithm is improved by random two-dimensional perturbation strategy.The flip-over process of monkey swarm algorithm is used to improve ant colony movement in poor region.According to the tabu list of ant colony optimization algorithm,the same point check scheme is designed.The computational complexity of the algorithm is further reduced by the same point check scheme and appropriate termination strategy.The algorithm implements the block matching motion estimation technique with higher precision and lower computational complexity.