Image vector quantization using classified binary-tree-structured self-organizing feature maps

被引：0

作者：

Chang, JS ^{[1
]}

Chiueh, TD ^{[1
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2000年 / E83D卷 / 10期

关键词：

classified vector quantization; edge degradation; SOFM; binary tree; progressive image transmission;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the continuing growth of the World Wide Web (WWW) services over the Internet, the demands for rapid image transmission over a network link of limited bandwidth and economical image storage of a large image database are increasing rapidly. In this paper, a classified binary-tree-structured Self-Organizing Feature Map neural network is proposed to design image vector codebooks for quantizing images. Simulations show that the algorithm not only produces codebooks with lower distortion than the weil-known CVQ algorithm[10] but also can minimize the edge degradation. Because the adjacent codewords in the proposed algorithm are updated concurrently, the codewords in the obtained codebooks tend to be ordered according to their mutual similarity which means more compression can be achieved with this algorithm. It should also be noticed that the obtained codebook is particularly well suited for progressive image transmission because it always forms a binary tree in the input space.

引用

页码：1898 / 1907

页数：10

共 50 条

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