Skip to main content
SpringerLink
Log in

Codebook optimization using genetic algorithm and simulated annealing

  • Conference paper
Thinkquest~2010

  • 1456 Accesses

Abstract

The demand for handling images in digital form has increased dramatically in recent years. The use of computer graphics in scientific visualization and engineering applications is growing at a rapid pace. Despite the advantages, there is one potential problem with digital images, namely, large number of bits required to represent them. Fortunately, digital images, in their canonical representation, generally contain a significant amount of redundancy. Image compression, is the art / science of efficient coding of picture data that aims at taking advantage of this redundancy to reduce the number of bits required to represent an image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Y. Linde, A. Buzo and R.M. Gray, “An algorithm for vector quantizer design,” IEEE Transactions on Communications, Vol. 28, No. 1, pp. 84-95, January 1980

    Article  Google Scholar 

  2. R. M. Gray, “Vector Quantization”, IEEE ASSP Magazine, Vol. 1, pp. 4–29, April 1984

    Article  Google Scholar 

  3. A. Gersho, “Principles of Quantization”, IEEE Transactions on Circuits and Systems, Vol. 25, No. 7, pp. 427–436, July 1978

    Article  Google Scholar 

  4. A. Gersho, “ On the structure of Vector Quantizers”, IEEE Transactions on Information Theory, Vol. 28, No. 2, pp. 157–166, March 1982

    Article  MathSciNet  Google Scholar 

  5. D. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison- Wesley Reading, MA, 1989

    Google Scholar 

  6. H. B. Kekre, Tanuja K. Sarode, “New Fast Improved Code-book Generation Algorithm for Color Images using Vector Quantization” International Journal of Engineering and Technology, vol. 1, Sept. 2008,pp.67-77

    Google Scholar 

  7. H. B. Kekre, Tanuja K. Sarode, “Fast Codebook Generation Algorithm for Color Images using Vector Quantization” International Journal of Computer Science and Information Technology (IJCSIT), Volume 1, Number 1, pp.7-12, Jan-June 2008

    Google Scholar 

  8. Mohammed A.F. Al-Husainy, “A Tool for Compressing Images Based on genetic Algorithm”, Information Technology Journal 6(3), 2007, pp. 457–462

    Article  Google Scholar 

  9. Wei Lu and Issa Traore, “Determining the optimal Number of Clusters Using a New Evolutionary Algorithm”, Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), IEEE, 2005

    Google Scholar 

  10. V Delport and M. Koschorreck, “Genetic Algorithm for Codebook Design in Vector Quantization”, Electronics Letters, Volume 31, No. 2, 1995

    Google Scholar 

  11. Liu Ying, Zhou Hui and Yu Wen-Fang, “Image Vector Quantization Coding Based on Genetic Algorithm”, Proceedings of the 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, pp. 773–777, October 2003

    Google Scholar 

  12. K Krishna and M. Narasimha Murty, “Genetic K-means Algorithm”, IEEE Transactions on Systems, Man and Cybernetics–Part B, Volume 29, No. 3, pp -433–439, June 1999

    Article  Google Scholar 

  13. J.S.Pan , F.R.McInnes and M.A.Jack , “VQ codebook design using genetic algorithms”, Electronic Letters, vol. 31, no.17,17th August 1995

    Google Scholar 

  14. Xiaowei Zheng, Bryant A. Julstrom, and Weidong Cheng, “Design of Vector Quantization Codebooks using a Genetic Algorithm”, IEEE 1997

    Google Scholar 

  15. Jianmin Jiang and Darren Butler, “A Genetic Algorithm Design for Vector Quantization”, Genetic Algorithms in Engineering Systems: Innovations and Applications, 12–14 September 1995, Conference Publication No. 414, IEE, 1995, pp. 331–336

    Google Scholar 

  16. Gao Li’ai, Zhang Shuguang, ZhouYongjie and Li Lihua, “A New Codebook Design Method Based on Genetic Programming”, The Eight International Conference on Electronic Measurement and Instruments, ICEMI’2007, pp. 3-250–3-253

    Google Scholar 

  17. Wee-Keong Ng, Sunghyun Choi, Chinya V. Ravishankar, “An Evolutionary Approach to Vector Quantizer Design”, IEEE International Conference on Evolutionary Computation, Volume 1, pp. 406–411, 29 Nov.–1 Dec. 1995

    Google Scholar 

  18. K Krishna, K R Ramakrishnan and M A L Thathachar, “Vector Quantization using Genetic K-Means Algorithm for Image Compression”, International Conference on Information, Communications and Signal Processing, ICICS’97, pp. 1585–1587, 9–12 September 1997

    Google Scholar 

  19. N. M. Nasrabadi and R. A. King, “Image Coding using Vector Quantization: A Review”, IEEE Transactions on Communications, Vol. 36, pp 957–971, August 1988

    Article  Google Scholar 

  20. Yoshitaka Takeda, Sinya Watanabe and Yukinori Suzuki, “Code Book Optimization with a Genetic Algorithm for Vector Quantization”, IEEE Conference on Soft Computing in Industrial Applications (SMCia/08), pp. 411–414, June 25-27, 2008

    Google Scholar 

  21. S. Kirkpatrick, C.D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing”, Science, Vol. 220, pp. 671–680, May 1983

    Article  MathSciNet  Google Scholar 

  22. Jacques Vaisey and Allen Gersho, “Simulated Annealing and Codebook Design”, IEEE, 1998

    Google Scholar 

  23. G. Phanendra Babu and M. Narasimha Murty, “Simulated Annealing for Selecting Optimal Initial Seeds in the K-Means Algorithm”, Indian Journal of Applied Mathematics, January & February 1994, pp. 85–94

    Google Scholar 

  24. J. K. Flanagan, D.R. Morrell, R.L. Frost, C.J. Read and B.E. Nelson, “Vector Quantization Codebook Generation Using Simulated Annealing”, IEEE, 1989

    Google Scholar 

  25. Peter W.M. Tsang and W.T. Lee, “Enhanced Hit Rate Simulated Annealing in codebook Training”, International Symposium on Signal Processing and its Applications, pp. 152 -154, August 1996

    Google Scholar 

  26. Abbas A. EL Gamal, L.A. Hemachandra, I. Shperling and V.K. Wei, “Using Simulated Annealing to Design Code-books”, IEEE Transactions on Information Theory, volume IT-33, no. 1, pp. 116–123, January 1987

    Google Scholar 

  27. Zhenya He, Chenwu Wu, Jun Wang and Ce Zhu, “A New Vector Quantization Algorithm Based on Simulated Annealing”, 1994 International Symposium on Speech, Image Processing and Neural Networks, Hong Kong, pp. 654–657, 13–16 April 1994

    Google Scholar 

  28. A. E. Cetin and V. Weerackody, “Design of Vector Quantizers using Simulated Annealing”, IEEE Transactions on Circuits and Systems, Vol. 35, pp.1550, December 1988

    Article  Google Scholar 

  29. Ngoc-Ai Lu and Darryl R. Morrell, “ VQ Codebook Design Using Improved Simulated Annealing Algorithms”

    Google Scholar 

  30. WEI Yanna and WAN G. Sheguo, “An Optimized Method of VQ Codebook Based on Genetic Algorithm”, Modern Electronics Technique, Beijing, No. 13, pp. 151–153, 2006

    Google Scholar 

  31. H. B. Kekre, Tanuja K. Sarode, “Fast Improved Clustering Algorithms for Vector Quantization”, National Conference on Image Processing, TSEC, India, Feb 2005

    Google Scholar 

  32. H. B. Kekre, Tanuja K. Sarode, “Fast Improved Clustering Algorithms for Vector Quantization”, NCSPA 2007, Padmashree Dr. D. Y. Patil Institute of Engineering and Technology, Pune, India, September 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MPSTME, NMIMS University, Mumbai, India

    Dr. H. B. Kekre (Senior Professor, Professor)

  2. Dept. of Information Technology, Thadomal Shahani Engg. College, Mumbai, India

    Chetan Agarwal

Authors
  1. Dr. H. B. Kekre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chetan Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Babasaheb Gawde Institute of Technology, Mumbai, India

    S. J. Pise

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer India Pvt. Ltd

About this paper

Cite this paper

Kekre, H.B., Agarwal, C. (2011). Codebook optimization using genetic algorithm and simulated annealing. In: Pise, S.J. (eds) Thinkquest~2010. Springer, New Delhi. https://doi.org/10.1007/978-81-8489-989-4_20

Download citation

  • DOI: https://doi.org/10.1007/978-81-8489-989-4_20

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-8489-988-7

  • Online ISBN: 978-81-8489-989-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation