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Image Compression for Color and Multispectral Image using Enhanced BTC

IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence
© 2015 by IJCA Journal
NCRIIAMI 2015 - Number 2
Year of Publication: 2015
C. Senthilkumar
S. Pannirselvam

C.senthilkumar and S.pannirselvam. Article: Image Compression for Color and Multispectral Image using Enhanced BTC. IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence NCRIIAMI 2015(2):11-12, June 2015. Full text available. BibTeX

	author = {C.senthilkumar and S.pannirselvam},
	title = {Article: Image Compression for Color and Multispectral Image using Enhanced BTC},
	journal = {IJCA Proceedings on National Conference on Research Issues in Image Analysis and Mining Intelligence},
	year = {2015},
	volume = {NCRIIAMI 2015},
	number = {2},
	pages = {11-12},
	month = {June},
	note = {Full text available}


Image Compression is a technique to reduce the number of bits required to represent and store an image. The Block Truncation Coding (BTC) is a streamlined and competent application for image compressions. Even though Simple BTC can apply to make enough compression on gray scale image, it needs to extend another improved version called Enhanced Block Truncation Coding (E-BTC) for color and multispectral image compression[1]. The given color and multispectral image are converted into component image and transformed into matrix format. Then the component image is divided into blocks. After finding block sum value, mean value and variance, the number of bits required to represent an image can be reduced by E-BTC model. The compressed binary values are stored in a table with reconstructed parameters. The binary values with parameters are passed to inverse E-BTC to reconstruct the sub image. The proposed algorithm is repeated for all remaining blocks and all are merged to get completely reconstructed image. Finally, compression ratio table is generated. This proposed E-BTC algorithm is tested and implemented on various parameters such as MSE, SNR, PSNR, BR and CR values. These experiments are carried out on the standard color image and multispectral image without loss of data as well as the quality of the image using MATLAB R2013a version 8. 1.


  • Aditya Kumar,Pardeep Singh, "Enhanced Block Truncation Coding for Gray Scale Image" Int. J. Comp. Tech. Appl. , Vol 2 (3), 525-529,2012-ISSN:2229-6093.
  • D. Ballard and C. Brow, Computer Vision, Prentice-Hall, Englewood Cliffs, NJ (1982).
  • A. K. Jain, A. Nassir and D. Nelson, "Multispectral feature display via Pseudo Coloring", Proceedings 24th Annual SPSE meeting, p,K-3.
  • Lucas Hui, "An Adaptive Block Truncation Coding Algorithm for Image Compression", IEEE, PP. 2233-2235,1990.
  • Maximo D. Lema, O. Robert Mitchell, "Absolute Moment Block Truncation Coding and its Application to Color Images", IEEE Transactions on Communications, VOL. COM-32, NO. 10, October 1984.
  • Mamta Sharma,"Compression Using Huffman Coding", IJCSNS International Journal of Computer Science and Network Security, VOL. 1 0 No. 5, May 2010.
  • Mohammed Al-laham & Ibrahiem M. M. El Emary. "Comparative Study Between Various Algorithms of Data Compression Techniques". Proceedings of the World Congress on Computer Science 2007 WCECS 2007, October-2007, San Francisco, USA.
  • Meftah M. Almrabet , Amer R. Zerek, Allaoua Chaoui Ali A. Akash "Image compression using block truncation coding" IJ-STA, Volume 3, No 2, December 2009.
  • Delp E. J. Mitchel O. R(1979) Image Coding using Block Truncation Coding, IEEE Transactions on Communications, 27, pp 1335-1342.
  • Pravin B. Pokle and Dr. Narendra. G. Bawane, "Comparative Study of Various Image Compression Techniques", International Journal of Engineering Research, Volume 4, Issue 5, May-2013.
  • Guoping Qiu, "Color image indexing using BTC", IEEE Transactions Image Processing Vol 12. No. 1 pp 93-101, January 2003.
  • K. Somasundaram and I. Kaspar Raj, "Low Computational Image Compression Scheme based on Absolute Moment Block Truncation Coding", World Academy of Science, Engineering and Technology, Vol. 19, pp. 166-171, 2006.
  • Sonal and Dinesh Kumar "A Study of Various Image Compression Techniques", Department of Computer Science & Engineering Guru Jhambheswar University of Science and Technology, Hisar, Issue 1, April 2011.
  • K. Somasundaram, Ms. S. Vimala "Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC" International Journal of Information Sciences and Techniques (IJIST) Vol. 2, No. 2, March 2012.
  • K. Somasundram and S. Vimala, "Efficient Block Truncation Coding", International Journal on Computer Science and Engineering, Vol. 02, No. 06, 2010, 2163-2166.
  • S. Vimala, P. Uma, B. Abidha "Improved Adaptive block truncation coding for image compression" international journal of computer application (0975-8887) vol 19-No. 7, April 2011.