Image Vector Quantization Using Classified Binary-Tree-Structured Self-Organizing Feature Maps

• CHANG Jyh-Shan

  Department of Electrical Engineering, National Taiwan University

• CHIUEH Tzi-Dar

  Department of Electrical Engineering, National Taiwan University

抄録

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 well-known CVQ algorithm[10]but also can minimize the edge degradation. Because the adjacent codewords in the proposed algorithm are updated concurrently, the code-words 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.

収録刊行物

• IEICE transactions on information and systems

  IEICE transactions on information and systems 83 (10), 1898-1907, 2000-10-25

  一般社団法人電子情報通信学会

キーワード

• classified vector quantization
• edge degradation
• SOFM
• binary tree, progressive image transmission

詳細情報

• CRID: 1572824502325065728
• NII論文ID: 110003210201
• NII書誌ID: AA10826272
• ISSN: 09168532
• 本文言語コード: en
• データソース種別:
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