An adaptive incremental LBG for vector quantization
An adaptive incremental LBG for vector quantization
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DOI：
10.1016/j.neunet.2005.05.001
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发表时间：
2006-06
期刊：
影响因子：
7.8
通讯作者：
S. Furao;O. Hasegawa
中科院分区：
文献类型：
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作者：
S. Furao;O. Hasegawa
本研究提出一种新的向量量化方法，产生码字递增。在输入向量空间的失真误差最高的区域中插入新的码字，直到达到期望的码字数量（或失真误差阈值）。自适应距离函数的采用大大提高了所提出的方法的性能。在增量过程中，使用删除插入技术来微调码本，使所提出的方法独立于初始条件。所提出的方法比一些最近发表的有效算法，如增强LBG（Patane，和Russo，2001年）的传统任务：固定数量的码字，找到一个合适的码本，以尽量减少失真误差。所提出的方法也可以用于新的任务，是无法使用传统的方法：与固定的失真误差，以尽量减少码字的数量，并找到一个合适的码本。对一些图像压缩问题的实验表明，该方法效果良好。
This study presents a new vector quantization method that generates codewords incrementally. New codewords are inserted in regions of the input vector space where the distortion error is highest until the desired number of codewords (or a distortion error threshold) is achieved. Adoption of the adaptive distance function greatly increases the proposed method's performance. During the incremental process, a removal–insertion technique is used to fine-tune the codebook to make the proposed method independent of initial conditions. The proposed method works better than some recently published efficient algorithms such as Enhanced LBG (Patane, & Russo, 2001) for traditional tasks: with fixed number of codewords, to find a suitable codebook to minimize distortion error. The proposed method can also be used for new tasks that are insoluble using traditional methods: with fixed distortion error, to minimize the number of codewords and find a suitable codebook. Experiments for some image compression problems indicate that the proposed method works well.