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Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06)
A Novel Generalized Particle Model for Lossless Data Compression
Las Vegas, Nevada
June 19-June 20
ISBN: 0-7695-2611-X
ASCII Text
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Dianxun Shuai, Qing Shuai, "A Novel Generalized Particle Model for Lossless Data Compression," Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on, pp. 202-207, Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06), 2006.
 
 
BibTex
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@article{ 10.1109/SNPD-SAWN.2006.6,
author = {Dianxun Shuai and Qing Shuai},
title = {A Novel Generalized Particle Model for Lossless Data Compression},
journal ={Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on},
volume = {0},
year = {2006},
isbn = {0-7695-2611-X},
pages = {202-207},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD-SAWN.2006.6},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on
TI - A Novel Generalized Particle Model for Lossless Data Compression
SN - 0-7695-2611-X
SP202
EP207
A1 - Dianxun Shuai,
A1 - Qing Shuai,
PY - 2006
KW - null
VL - 0
JA - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, International Conference on & Self-Assembling Wireless Networks, International Workshop on
ER -
 
Dianxun Shuai, East China University of Sci. and Tech., China
Qing Shuai, Huazhong University of Sci. and Tech., China
This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. Local rules for particle movement in GPM, parallel algorithm and its implementation structure to generate the desired predictive coding are discussed. The proposed GPM approach has advantages in terms of encoding speed, parallelism, scalability, simplicity, and easy hardware implementation over other sequential lossless compression methods.
Citation:
Dianxun Shuai, Qing Shuai, "A Novel Generalized Particle Model for Lossless Data Compression," snpd-sawn, pp.202-207, Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06), 2006
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