Two fast nearest neighbor searching algorithms for image vector quantization
Two fast nearest neighbor searching algorithms for image vector quantization
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DOI：
10.1109/26.545888
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发表时间：
1996-12-01
影响因子：
8.3
通讯作者：
Lin, YC
中科院分区：
文献类型：
--
作者：
Tai, SC;Lai, CC;Lin, YC
本文提出了矢量量化（VQ）的两种高效码本搜索算法，第一种快速搜索算法利用信号能量在变换域上的紧性和输入矢量与各协矢量之间的几何关系，消除了与输入矢量不可能成为最接近码字的协矢量，达到了完全的搜索等效性能，与其他同类快速搜索方法相比；在此基础上，提出了一种牺牲重构信号质量以加快最近邻搜索速度的次优搜索方法，该算法在预定义的小子码本上进行搜索，而不是对最近邻的整个码本进行搜索。实验结果表明，与树状VQ相比，该方法不仅需要更少的CPU时间来编码图像，而且重构信号质量的损失也更小。
In this paper, two efficient codebook searching algorithms for vector quantization (VQ) are presented, The first fast search algorithm utilizes the compactness property of signal energy on transform domain and the geometrical relations between the input vector and every codevector to eliminate those codevectors that have no chance to be the closest codeword of the input vector, It achieves a full search equivalent performance, As compared with other fast methods of the same kind, this algorithm requires the fewest multiplications and the least total times of distortion measurements, Then, a suboptimal searching method, which sacrifices the reconstructed signal quality to speed up the search of nearest neighbor, is presented, This algorithm performs the search process on predefined small subcodebooks instead of the whole codebook for the closest codevector, Experimental results show that this method not only needs less CPU time to encode an image but also encounter less loss of reconstructed signal quality than tree-structured VQ does.