The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Avec la croissance continue des services du World Wide Web (WWW) sur Internet, les demandes de transmission rapide d'images sur une liaison réseau à bande passante limitée et de stockage d'images économique d'une grande base de données d'images augmentent rapidement. Dans cet article, un réseau neuronal de carte de caractéristiques auto-organisatrices structuré en arbre binaire est proposé pour concevoir des livres de codes vectoriels d'images pour quantifier les images. Les simulations montrent que l'algorithme produit non seulement des livres de codes avec une distorsion plus faible que l'algorithme CVQ bien connu, mais qu'il peut également minimiser la dégradation des bords. Étant donné que les mots de code adjacents dans l'algorithme proposé sont mis à jour simultanément, les mots de code dans les livres de codes obtenus ont tendance à être classés en fonction de leur similarité mutuelle, ce qui signifie qu'une plus grande compression peut être obtenue avec cet algorithme. Il convient également de noter que le livre de codes obtenu est particulièrement bien adapté à la transmission progressive d'images car il forme toujours un arbre binaire dans l'espace d'entrée.
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Jyh-Shan CHANG, Tzi-Dar CHIUEH, "Image Vector Quantization Using Classified Binary-Tree-Structured Self-Organizing Feature Maps" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 10, pp. 1898-1907, October 2000, doi: .
Abstract: 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 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_10_1898/_p
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@ARTICLE{e83-d_10_1898,
author={Jyh-Shan CHANG, Tzi-Dar CHIUEH, },
journal={IEICE TRANSACTIONS on Information},
title={Image Vector Quantization Using Classified Binary-Tree-Structured Self-Organizing Feature Maps},
year={2000},
volume={E83-D},
number={10},
pages={1898-1907},
abstract={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 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.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Image Vector Quantization Using Classified Binary-Tree-Structured Self-Organizing Feature Maps
T2 - IEICE TRANSACTIONS on Information
SP - 1898
EP - 1907
AU - Jyh-Shan CHANG
AU - Tzi-Dar CHIUEH
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2000
AB - 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 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.
ER -