Color image vector quantization using binary tree structured self-organizing feature maps

被引：0

作者：

Chang, JS ^{[1
]}

Lin, JHJ ^{[1
]}

Chiueh, TD ^{[1
]}

机构：

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

来源：

IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE | 1998年

关键词：

vector quantization; SOFM; binary tree; progressive image transmission;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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 is increasing rapidly. In this paper, a binary tree structured Self-Organizing Feature Map neural network is proposed to design the image vector codebook for quantizing color images. Simulations show that the algorithm not only produces codebooks with lower distortion than the well-known GLA-T algorithm but also performs better in differential index entropy 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 forms a binary tree in the input space.

引用

页码：1428 / 1432

页数：5

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