矢量量化编码过程中需要进行大量的矢量间距离计算,这个过程的计算复杂度极高,严重限制了其实际使用。为了加速矢量量化的编码过程,已经提出了各种基于1维特征量的码字搜索算法来减小码字搜索的范围。本文在基于不等式的快速搜索算法基础上,通过使用更有效的基于特征量的搜索算法,并引入自适应子矢量划分的方法,将额外增加的存储空间从N(N－1)/2 降低到了 13N,码字搜索范围减小了33.88%50.94%,编码时间减少了10.82%27.16%。

In the encoding process of vector quantization (VQ), a great deal of distance computations between vectors are needed, which is computationally expensive and prevents its practical applications. In order to speed up the VQ encoding process, lots of fast codeword search algorithms based on 1-D characteristics have been proposed for reducing the codeword search space. This paper aims at improving the method using inequality proposed by Mu et al by using more effective method based on 1-D characteristics and adaptive subvector povrtitions. The experimental results show that our proposed scheme can reduce the extra memory requirment from N(N-1)/2 to 13N, meanwhile reducing the codeword search space by 33.88%50.94% and reducing the encoding time by 10.82%27.16%.