L BG算法作为矢量量化的基本算法具有经典意义，但由于在训练图象中，总存在少量的离群矢量，使得在训练码书时，码字的分布受到影响，进而使得压缩性能下降，因而不能充分体现出矢量量化的优越性能.而运用基于稳健统计的方法来设计矢量量化器，由于减少了码书中的离群矢量，同时加强了中心矢量在码书中的权重，因而不仅能够尽量减少码书的冗余，而且能大幅度提高压缩性能.实验结果显示，用基于稳健统计的设计方法设计的码书，其压缩性能比传统的 L BG算法有了较大的改善，且恢复图象的主观、客观效果都是令人满意的.

LBG algorithm is classical algorithm in Vector Quantization, which was proposed in 1980 by Y. Linde, A. Buzo, and R. M. Gray. Used this algorithm, an acceptable performance codebook can be get in the acceptable time, it has better performance than Scale Quantization that was proved by Shannon. But few outlines vector of the training image effect the distribution of the codeword in the codebook. To few vectors, it maybe noises, but have large codeword number in the codebook. And decrease the body vectors' codeword in the codebook. That decreases the compress ratio and makes the reconstruct image worse, the advantageous of the Vector Quantization can't be explained adequately. Different people used this algorithm with different image can get different compression ratio. Design the Vector Quantizer based on robust statistic can improve it. Decrease the outlines vectors, improve the center vector effect in the codebook, it can decrease the relativity of the codebook, made the distribution of the codeword of the codebook more economical and bring on the compression ratio. Theoretical analysis and simulation experimental results presented in the paper show that this method can obtain good reconstruction image quality and high compress ratio. It is improved in both subjective and objective.1