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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dengan pertumbuhan berterusan perkhidmatan World Wide Web (WWW) melalui Internet, permintaan untuk penghantaran imej pantas melalui pautan rangkaian jalur lebar terhad dan storan imej ekonomi bagi pangkalan data imej yang besar meningkat dengan pesat. Dalam makalah ini, rangkaian neural Peta Ciri Penyusunan Sendiri binari-pokok terperingkat dicadangkan untuk mereka bentuk buku kod vektor imej untuk mengkuantiti imej. Simulasi menunjukkan bahawa algoritma bukan sahaja menghasilkan buku kod dengan herotan yang lebih rendah daripada algoritma CVQ yang terkenal tetapi juga boleh meminimumkan kemerosotan kelebihan. Oleh kerana kata kod bersebelahan dalam algoritma yang dicadangkan dikemas kini secara serentak, kata kod dalam buku kod yang diperoleh cenderung untuk disusun mengikut persamaan bersama mereka yang bermakna lebih banyak pemampatan boleh dicapai dengan algoritma ini. Perlu juga diperhatikan bahawa buku kod yang diperolehi amat sesuai untuk penghantaran imej progresif kerana ia sentiasa membentuk pokok binari dalam ruang input.
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Salinan
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
Salinan
@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},}
Salinan
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 -