中文摘要：

本文给出了一种新的图像矢量量化码书的优化设计方法——粒子对算法．在传统粒子群优化（Patticle Swarm Optimization，PSO）算法的基础上，用两个粒子构成了群体规模较小的粒子对，在码书空间中搜索最佳码书．在每次迭代运算中，粒子对按先后顺序执行PSO算法中的速度更新、位置更新操作和标准LBG算法，并用误差较大的训练矢量代替越界的码字．此算法避免粒子陷入局部最优码书，较准确地记录和估计每个码字的最佳移动方向和历史路径，在训练矢量密集区域和稀疏区域合理地分配码字，从而使整体码书向全局最优解靠近．实验结果表明，本算法始终稳定地取得显著优于FKM、FRLVQ、FRLVQ-FVQ算法的性能，较好地解决了矢量量化中初始码书影响优化结果的问题，且在计算时间和收敛速度方面有相当的优势．

英文摘要：

This paper presents a new strategy of particle-pair （PP） for vector quantization （VQ） in image coding. In this strategy, two particles are combined into a particle-pair based on conventional particle swarm optimization （PSO） algorithm. At each iteration, the particle-pair performs basic operations of PSO （velocity updating and position updating） and conventional LBG algodthrn in sequence. The codevectors flying over the boundary are replaced with the training vectors, which have large distortions. This strategy prevents the particle from being trapped in a local optimum, memorizes and estimates the best direction the particle moves toward to find the optimum codebook design. The codevectors are scattered reasonably both in high density distribution regions and low density areas of the training vector space. Experimental results have demonstrated that the performance of this new algorithm is much better than that of FKM, FRLVQ,FRLVQ-FVQ consistently with shorter computational time and higher convergence rate,and the dependence of the final optimum codebook on the selection of the initial codebook is reduced effectively.