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Improved Initial-codebooks for LBG Algorithm

机译:LBG算法的改进的初始码本

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摘要

In this paper, we, starting from space distribution of trained vectors, generate better initial codebooks so that LEG algorithm with our initial codebooks can optimize the performance of vectors quantization. More precisely, we obtain N_1 cuts by applying theory of fuzzy sets as a cell partition of trained vectors space. By controlling A -cut level dynamically, the number of cuts and size of corresponding cell are determined. Then N_1 centers are found out as representative vectors (N_1 should be greater than the size N of initial codebooks) and its probability distribution is calculated. In a suitable manner, N_1 representative vectors are merged into N vectors which serve as initial codebooks. The tests show that the value of SNR increases by 0.5dB or so.
机译:在本文中,我们从训练向量的空间分布出发,生成了更好的初始码本,以便带有我们初始码本的LEG算法可以优化向量量化的性能。更准确地说,我们通过应用模糊集理论作为训练向量空间的单元划分来获得N_1个割。通过动态控制剪切级别,可以确定剪切的数量和相应像元的大小。然后找到N_1个中心作为代表向量(N_1应该大于初始码本的大小N),并计算其概率分布。以合适的方式,将N_1个代表性矢量合并为用作初始码本的N个矢量。测试表明,SNR值增加了0.5dB左右。

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