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基于改进粒子群算法的矢量量化码书设计研究

Codebook Design of Vector Quantization Based on Improved Particle Swarm Optimization
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摘要 提出一种粒子群分组并行寻优码书设计算法,应用于图像的矢量量化编码中。可以得到性能较好的码书。利用同一种群两个分组分别进化,同时相互监督,某一个分组或者两个分组都陷入局部最优时,它能够通过相互作用跳出局部最优;然后通过对训练矢量进行排序,合理选择初始码书,使码字的分布更加合理,增强搜索多样性;最后通过仿真实验验证了该改进算法的合理性。 Swarm optimization algorithm based on a pair of parallel particles is presented,which can be used to get a good codebook in the vector quantization of image coding.Using the two groups evolve separately and monitoring each other,when one or two groups fell into local optimum,it could jump out of local optimum through interaction;By sorting the training vector,the initial codebook is selected to make the code distribution more reasonable and enhance the diversity of search;Finally,the simulation results prove that the improved method is reasonable.
出处 《科学技术与工程》 2010年第28期7022-7025,7030,共5页 Science Technology and Engineering
基金 广东省自然科学基金(9151064101000037)资助
关键词 矢量量化 分组寻优 码书设计 局部最优 vector quantization optimization by groups codebook design local optimum.
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参考文献5

  • 1陈善学,李方伟.矢量量化与图像处理.北京:科学出版社,2009;61-67.
  • 2Eberhart R C,Kennedy J.A new optimization using particle swarm theory.Proceeding of Sixth International Symposium on Micro Machine and Human Science,1995;39-43.
  • 3Shi Y,Eberhart R C.Empirical study of particle swarm optimization.Proceeding of the Congress on Evolutionary Computation,1999;69-73.
  • 4Shi Y,Eberhart R C.Fuzzy adaptive particle swarm optimization.Proceeding of the IEEE Conference on Evolutionary Computation,Seoul Korea,2001;10:1-10.
  • 5朱海梅,吴永萍.一种高速收敛粒子群优化算法[J].控制与决策,2010,25(1):20-24. 被引量:65

二级参考文献16

  • 1Kennedy J, Eberhart R. Particle swarm optimization [C]. Proc of IEEE Int Conf on Piscataway, 1995:1942- 1948.
  • 2Eberhart R C, Shi Y. Particle swarm optimization[C]. Proc of Congress on Evolutionary Computation. Seoul, 2001: 81-88.
  • 3Angeline PJ. Evolutionary optimization versus particle swarm optimization [ C]. Evolutionary Programming VII. London: Springer, 1998: 601-610.
  • 4Shi Y, Eberhart R. Parameter selection in particle swarm optimization[C]. Proc of 7th Annual Conf on Evolution Computation. Berlin, 1998: 591-601.
  • 5Shi Y, Eberhart R. Empirical study of particle swarm optimization [C]. Proc of the 1999 Congress on Evolution Computation. Berlin, 1999: 1945-1950.
  • 6Kennedy J, Eberhart R, Shi Y. Swarm Intelligence [M]. San Francisco: Morgan Kaufmann Publishers, 2001.
  • 7Zheng Y L, Ma I. H. Convergence analysis and parameter selection in particle swarm optimization[C]. Proc of the Second Int Conf on Machine Learning and Cybernetics. Xi'an, 2003: 1802-1807.
  • 8Lei W, Zhao C X. The research of PSO algorithms with non-linear time-decreasing inertia [C]. Proc of the Int Conf on Intelligent Control and Automation. Chongqing, 2008: 4002-4005.
  • 9Chen D, Wang G F, Chen Z Y. The inertia weight selfadapting in PSO[C]. Proc of the 7th World Congress on Intelligent Control and Automation. Chongqing, 2008: 5313-5316.
  • 10Feng C S, Cong S, Feng X Y. A new adaptive inertia weight strategy in particle swarm optimization [C]. Proc of the IEEE Congress on Evolutionary Computation. Singapore, 2007: 4186-4190.

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