【摘要】 模拟退火是解决全局优化问题的随机搜索技术,它在工程领域有着广泛应用。将模拟退火技术与LBG算法有机结合,通过对LBG算法中的空胞腔码书进行适当处理,并设置适当参数使LBG与模拟退火交替进行,提出了一种快速模拟退火矢量量化图像编码算法,以降低计算量和进一步提高码书性能。实验结果表明,算法具有对初始码书选取依赖性小、不容易陷入局部极小、收敛速度快等优点。与随机竞争学习矢量量化及进化模拟退火矢量量化算法相比,峰值信噪比改善超过0.6dB。更多还原

【Abstract】 Simulated annealing is a stochastic search algorithm for solving the global optimization problems. It has been widely used in various engineering areas. A fast simulated annealing vector quantization algorithm is proposed in this paper,which combines the LBG algorithm with simulated annealing to reduce the computation load and improve the codebook抯 performance of vector quantization for image coding. The novel point lies in the processing of codevector whose voronoi is empty in the LBG algorithms and the selection of two parameters to transit between simulated annealing and LBG algorithms. Simulation results show that the algorithm can eliminate the effect of initial codebook selection on the quality of clustering,and avoid local minima,converge fast and attain a codebook with a better performance. The PSNR improvement of the proposed algorithm exceeds 0.6dB compared with those of SCLA and ESA algorithms.更多还原