New Research on Scalability of Lossless Image
Compression by GP Engine
J. He, X. Wang, M. Zhang, J. Wang, and Q. Fang. Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware, page 160--164. Washington, DC, USA, IEEE Press, (29 June-1 July 2005)
Abstract
By introducing the optimal linear predictive code
technic into the dynamic issue of loss less image
compression, this paper presented a less complexity
fitness function for Genetic Programming engine, which
can reduce the cost of computational time in each
evaluation for individual greatly, and can also provide
further benefit with the scalability issue. To make the
speed of large image compression faster in condition of
not increasing the cost of computational resource and
time, evaluating mechanism in the field of machine
learning was used to help Genetic Programming, and the
scalability issue was mapped to the task of making the
approach accuracy best from lower speed sampling to
higher speed sampling in the field of signal
processing. In experiments for compressing large
images, the cost of computational time was reduced
evidently and efficiently.
%0 Conference Paper
%1 he:2005:EH
%A He, Jingsong
%A Wang, Xufa
%A Zhang, Min
%A Wang, Jiying
%A Fang, Qiansheng
%B Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware
%C Washington, DC, USA
%D 2005
%E Lohn, Jason
%E Gwaltney, David
%E Hornby, Gregory
%E Zebulum, Ricardo
%E Keymeulen, Didier
%E Stoica, Adrian
%I IEEE Press
%K EHW algorithms, genetic programming,
%P 160--164
%T New Research on Scalability of Lossless Image
Compression by GP Engine
%U http://doi.ieeecomputersociety.org/10.1109/EH.2005.35
%X By introducing the optimal linear predictive code
technic into the dynamic issue of loss less image
compression, this paper presented a less complexity
fitness function for Genetic Programming engine, which
can reduce the cost of computational time in each
evaluation for individual greatly, and can also provide
further benefit with the scalability issue. To make the
speed of large image compression faster in condition of
not increasing the cost of computational resource and
time, evaluating mechanism in the field of machine
learning was used to help Genetic Programming, and the
scalability issue was mapped to the task of making the
approach accuracy best from lower speed sampling to
higher speed sampling in the field of signal
processing. In experiments for compressing large
images, the cost of computational time was reduced
evidently and efficiently.
%@ 0-7695-2399-4
@inproceedings{he:2005:EH,
abstract = {By introducing the optimal linear predictive code
technic into the dynamic issue of loss less image
compression, this paper presented a less complexity
fitness function for Genetic Programming engine, which
can reduce the cost of computational time in each
evaluation for individual greatly, and can also provide
further benefit with the scalability issue. To make the
speed of large image compression faster in condition of
not increasing the cost of computational resource and
time, evaluating mechanism in the field of machine
learning was used to help Genetic Programming, and the
scalability issue was mapped to the task of making the
approach accuracy best from lower speed sampling to
higher speed sampling in the field of signal
processing. In experiments for compressing large
images, the cost of computational time was reduced
evidently and efficiently.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Washington, DC, USA},
author = {He, Jingsong and Wang, Xufa and Zhang, Min and Wang, Jiying and Fang, Qiansheng},
biburl = {https://www.bibsonomy.org/bibtex/2888d7e53a5a00921e34fdecb54d7604a/brazovayeye},
booktitle = {Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware},
editor = {Lohn, Jason and Gwaltney, David and Hornby, Gregory and Zebulum, Ricardo and Keymeulen, Didier and Stoica, Adrian},
interhash = {0042bf02f6e94428350dbd53fdc5bde3},
intrahash = {888d7e53a5a00921e34fdecb54d7604a},
isbn = {0-7695-2399-4},
keywords = {EHW algorithms, genetic programming,},
month = {29 June-1 July},
notes = {EH2005 IEEE Computer Society Order Number P2399},
organisation = {NASA, DoD},
pages = {160--164},
publisher = {IEEE Press},
publisher_address = {IEEE Service Center 445 Hoes Lane Asia P.O. Box
1331 Piscataway, NJ 08855-1331},
timestamp = {2008-06-19T17:41:15.000+0200},
title = {New Research on Scalability of Lossless Image
Compression by {GP} Engine},
url = {http://doi.ieeecomputersociety.org/10.1109/EH.2005.35},
year = 2005
}