An Efficient Image Compression Technique with Dead Zone Quantization Through Wavelet-Based Contourlet Transform with Modified SPIHT Encoding

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Computer graphics applications especially the applications which produce complex color images produce very large file sizes. Data with very large size creates several problems such as requirement of very large storage space, the need to quickly transmit image data across networks, etc. Hence, image compression has become an active area of research in the field of image processing to reduce file size. Wavelet and curvelet transformations are widely used transformations techniques to carry out compression. But both have their own limitations which affects overall performance of the compression process. This research work focuses on presenting a non-linear image compression technique that compresses image both radically and angularly. Wavelet-based Contourlet Transformation (WBCT) has the potential to approximate the natural images comprising contours and oscillatory patterns. In addition to this transformation deadzone quantization technique is used to eliminate the redundancies in the images. Finally, this technique uses Modified Set Partitioning in Hierarchical Trees (MSPIHT) for efficient encoding process. The experimental results of wavelet-based contourlet transformation with deadzone quantization and MSPIHT are better when compared with existing transformations techniques.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Curvelet Transform; Quantization; DWT; SPIHT; Contourlet Transformation

Full Text:



S. Ragab, A. S. A. Mohamed, M. S. Hamid, “Efficiency of Analytical Transforms for Image Compression”, Radio Science National Conference - NSRC, 1998.

Beladgham, M., Bessaid, A., Moulay-Lakhdar, A., Taleb-Ahmed, A., Medical image compression using quincunx wavelets and VQ coding, (2010) International Review on Computers and Software (IRECOS), 5 (6), pp. 601-608.

M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Of Image Processing, vol.14, no.12, pp. 2091-2106, Dec. 2004.

A. Said and W.A. Pearlman, “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Transactions on Circuits and Systems for Video Technology, 1996.

Meyer, F.G., Averbuch, A.Z. and Coifman, R.R., “Multilayered image representation: application to image compression”, IEEE Transactions on Image Processing, Volume: 11, Issue: 9, Page(s): 1072 – 1080, 2002.

Jianhua Lin, “Adaptive image quantization based on learning classifier systems”, Data Compression Conference, 1995.

Robertson, M.A. and Stevenson, R.L., “DCT quantization noise in compressed images”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 15, Issue: 1, Page(s): 27- 38, 2005.

Jinhua Yu, “Advantages of uniform scalar dead-zone quantization in image coding system”, International Conference on Communications, Circuits and Systems, Volume: 2, Page(s): 805 – 808, 2004.

Yuanyuan Xu; Yao Zhao; “Three-Description Image Coding Using Optimal Dead-Zone Lattice Vector Quantization”, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2007), Volume: 1, Page(s): 375 – 378, 2007.

S. Esakkirajan, T. Veerakumar, V. Senthil Murugan and R. Sudhakar, “Fingerprint Compression Using Contourlet Transform and Multistage Vector Quantization”, International Journal of Biological and Life Sciences 1:2 2005.

D.L. Donoho and M. Duncan. Digital Curvelet Transform: Strategy, Implementation, and Experiments. in Proc. Aerosense 2000, Wavelet Applications VII, SPIE, 4056, 2000.

P. Vetrivelan and S. Subha Rani, “Wavelet Based Contourlet Transform for Image Compression”, Proceedings of the International Conference on Cognition and Recognition,

Osslan Osiris Vergara Villegas,

Vianey Guadalupe and Cruz Sánchez, “The wavelet based contourlet transform and its application to feature preserving image coding”, MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence, Pages 590-600, 2007.

Xi Zhi-hong and Xiao Yi-han, “An Image Compression Scheme Adopted for Contourlet Transform”, Congress on Image and Signal Processing - CISP, 2009.

A. Aydin Alatan, Minyi Zhao, Ali N. Akansu, “Unequal Error Protection Of SPIHT Encoded Image Bit Streams”, IEEE Journal on Selected Areas in Communications, Volume 18 Issue 6, pp. 814-818, 2006.

Wang Shen; Zhang Ye, “SPIHT coding image transmission based on resynchronization structure”, 2nd International Symposium on Systems and Control in Aerospace and Astronautics, Page(s): 1 – 5, 2008.

C.S. Burrus, R.A. Gopinath and H. Guo, “Introduction to Wavelets and Wavelet Transforms”, Primer, Prentice Hall, New Jersey, 1998.

S. Saha, “Image Compression from DCT to Wavelets: A Review”,, 2001.

Ramin Eslami and Hayder Radha, “Wavelet-based Contourlet Coding Using an SPIHT-like Algorithm”,

P S Arun Kumar, “Implementation of Image Compression Algorithm using Verilog with Area, Power and Timing Constraints”, 2009.

Balasubramanian, R.; Bouman, C.A.; Allebach, J.P.; “Sequential scalar quantization of vectors: an analysis”, IEEE Transactions on Image Processing, Volume: 4, Issue: 9, Page(s): 1282 - 1295, 1995.

Soo-Chang Pei; Ching-Min Cheng; “Dependent scalar quantization of color images”, IEEE Transactions on Circuits and Systems for Video Technology, Volume: 5 , Issue: 2, Page(s): 124 - 139, 1995.

Zaid, A.O.; Olivier, C.; Marmotton, F.; “Wavelet image coding with adaptive dead-zone selection: application to JPEG2000”, Proceedings of International Conference on Image Processing, Volume: 3, 2002.

T. Rammohan and K. Sankaranarayanan, “An Advanced Curvelet Transform Based Image Compression using Dead Zone Quantization”, European Journal of Scientific Research, Vol.79 No.4 (2012), pp.486-496, 2012.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize