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Reseach Article

Effect of Meta-Heuristics Swarm based Algorithm on DCT and DWT for Best Compressed Image

Published on July 2013 by Harsha D. Zope, Jasvinder Pal Singh
International Conference and workshop on Advanced Computing 2013
Foundation of Computer Science USA
ICWAC - Number 4
July 2013
Authors: Harsha D. Zope, Jasvinder Pal Singh
2dcba1cc-b604-409d-a378-a639cdc25277

Harsha D. Zope, Jasvinder Pal Singh . Effect of Meta-Heuristics Swarm based Algorithm on DCT and DWT for Best Compressed Image. International Conference and workshop on Advanced Computing 2013. ICWAC, 4 (July 2013), 0-0.

@article{
author = { Harsha D. Zope, Jasvinder Pal Singh },
title = { Effect of Meta-Heuristics Swarm based Algorithm on DCT and DWT for Best Compressed Image },
journal = { International Conference and workshop on Advanced Computing 2013 },
issue_date = { July 2013 },
volume = { ICWAC },
number = { 4 },
month = { July },
year = { 2013 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwac/number4/499-1308/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and workshop on Advanced Computing 2013
%A Harsha D. Zope
%A Jasvinder Pal Singh
%T Effect of Meta-Heuristics Swarm based Algorithm on DCT and DWT for Best Compressed Image
%J International Conference and workshop on Advanced Computing 2013
%@ 2249-0868
%V ICWAC
%N 4
%P 0-0
%D 2013
%I International Journal of Applied Information Systems
Abstract

The objective of image compression is to reduce irrelevance and redundancy of the image data in order to to store or transmit data in efficient form. DCT and DWT are used as the compression techniques. In discrete wavelet transform, each level is calculated by passing only approximation coefficients through low and high pass quadrature mirror filters. The discrete cosine transform (DCT) helps to separate the image into parts (or spectral sub-bands) of differing importance (with respect to the image's visual quality)In this paper , a meta-heuristic swarm based algorithm (ABC) is used to improve the quality of compressed image. Relative data redundancy and many parameters are also studied.

References
  1. BahriyeAkay, DervisKaraboga,"Wavelet packets optimization using Artificial Bee Colony algorithm" Evolutionary Computation (CEC), IEEE Congress,pp. 89-94,July 2011.
  2. Chenshuwang An Tao Haolitao ," Discrete Cosine Transform Image Compression Based on Genetic Algorithm"IEEE,pp. 1-3,Dec 2009
  3. HyungW. Paik and Anil Khubchandani,"QUANTIZATION SCHEME FOR JPEG BASELINE SEQUENTIAL ENCODING OF STILL IMAGES",pp976-979,IEEE 1992
  4. Kwo-Jyr Wong and C. -C. Jay Kuo " Image Compression with Fully-Decomposed Wavelet Transform. "IEEE,vol 3 ,pp. 1136 - 1140 ,Dec1992
  5. Anshul Singh, Deveshnarayan, International Journal of Emerging Technology and Advanced Engineering "A Survey Paper on Solving Travelling Salesman problem Using Bee colony optimization" Issue 5,Volume 2,pp-309-311,May 2012.
  6. Geo Peng,ChenWenming,LiangJian,, "Global Artificial Bee colony Search algorithm for numerical function optimization" Seventh International conference on natural computation,Volume 3,pp-1280 - 1283,July 2011
  7. D. Karaboga,B. Basturk, A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm,Journal of Global Optimization ,Issue 3,Volume 39,nov2007,pp-459-471
  8. D. Karaboga,BBasturk ,on the petformance of artificial bee colony(ABC)algorithm,Applied soft computing ,Issue 1,Volume 08,jan 2008,pp-687-697
  9. J. Kennedy, R. C. Eberhart, "Particle swarm optimization", In Proceedings of the1995 IEEE International Conference on Neural Networks", Vol. 4, pp. 1942–1948.
  10. E. Bonabeau, M. Dorigo, G. Theraulaz, "Swarm Intelligence: From Natural to Artificial Systems", New York, NY: Oxford University Press, 1999.
  11. Website www. mathwork. com on 2Jan2012 at 8. 30 pm
  12. CoifmanRR &Wickerhauser MV,1992. Entropy-Based Algorithm for Best Basic Selection,IEEE Transaction on information theory,38(2)
  13. NadezdaStanarevic, Milan Tuba, and NebojsaBacanin, International Journal Of Mathematical Model And Methods In Applied Science "Modified artificial bee colony algorithm for constrained problems optimization"Issue 3,Volume 5,2011 644
  14. F. W. Moore, A genetic algorithm for optimized reconstruction of quantized signals, Evolutionary Computation, 2005. The 2005IEEE Congress on, vol. 1, pp. 100-105,2005.
  15. D. Karaboga, B. BasturkAkay, Artificial Bee Colony Algorithm on Training Artificial Neural Networks, Signal Processing and Communications Applications, 2007. SIU 2007, IEEE 15th. 11–13 June 2007, Page(s):1 – 4.
  16. D. Karaboga, B. BasturkAkay, C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, Vol: 4617/2007, pp:318–319, Springer-Verlag, 2007, MDAI 2007.
  17. Y. Zhang, L. Wu, and S. Wang, Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm, Progress in Electromagnetics Research, vol. 116, (2011), pp. 65-79
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform Discrete Wavelet Transform Wavelet packet decomposition Artificial Bee Colony Algorithm Optimization algorithms