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快速树状最近邻编码的矢量量化。

Fast tree-structured nearest neighbor encoding for vector quantization.

机构信息

Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA.

出版信息

IEEE Trans Image Process. 1996;5(2):398-404. doi: 10.1109/83.480778.

Abstract

This work examines the nearest neighbor encoding problem with an unstructured codebook of arbitrary size and vector dimension. We propose a new tree-structured nearest neighbor encoding method that significantly reduces the complexity of the full-search method without any performance degradation in terms of distortion. Our method consists of efficient algorithms for constructing a binary tree for the codebook and nearest neighbor encoding by using this tree. Numerical experiments are given to demonstrate the performance of the proposed method.

摘要

本文研究了具有任意大小和向量维度的非结构化码本的最近邻编码问题。我们提出了一种新的树状最近邻编码方法,该方法在不降低失真性能的情况下,显著降低了全搜索方法的复杂度。我们的方法包括用于构建码本二叉树和使用该树进行最近邻编码的有效算法。数值实验证明了所提出方法的性能。

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