Compression of Color Image by Using Dual Tree Complex Wavelet Transfrom

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)


In this paper, we explore the use of Dual tree Complex wavelet Transform which is nearly shift invariant and directionally selective in two and higher dimensions. The multidimensional dual tree CWT is a nonseparable and is based on computationally efficient, Separable filter bank (FB). This paper describes how the complex wavelet transform with directional properties is designed and use of it in image compression. When we take the dual tree complex wavelet transform then many wavelet coefficients are close to zero and have intra-subband dependency. We further evaluate the performance of SPIHT coding schema for coding of those coefficients. The result of proposed schema gives higher rate of compression and lover MSE compared to the schema based of DWT. Dual tree complex wavelet transform-SPIHT schema outperform DWT based schema at lower bit rates.


Image compression Complex wavelet transform Image texture Dual tree SPIHT 


  1. 1.
    Taubman, D., Marcellin, M.: JPEG2000: Image Compression Fundamentals, Standards, and Practice. Kluwer, Norwell (2001)Google Scholar
  2. 2.
    Taubman, D., Zakhor, A.: Orientation adaptive subband coding of images. IEEE Trans. Image Process. 3(4), 421–437 (1994)CrossRefGoogle Scholar
  3. 3.
    Taubman, D.: Adaptive nonseparable lifting transforms for image compression. In: Proceedings IEEE International Conference Image Process, Kobe, Japan, 772–776, 3 Oct 1999Google Scholar
  4. 4.
    Claypoole, R.L., Davis, G.M., Sweldens, W., Baraniuk, R.G.: Nonlinear wavelet transforms for image coding via lifting. IEEE Trans. Image Process. 12(12), 1449–1459 (2003)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gerek, O.N., Cetin, A.E.: A 2-D orientation-adaptive prediction filter in lifting structures for image coding. IEEE Trans. Image Process. 15(1), 106–111 (2006)CrossRefGoogle Scholar
  6. 6.
    Ding, W., Wu, F., Wu, X., Li, S., Li, H.: Adaptive directional lifting-based wavelet transform for image coding. IEEE Trans. Image Process. 16(2), 416–427 (2007)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chang, C.L., Maleki, A., Girod, B.: Adaptive wavelet transform for image compression via directional quincunx lifting. In: Proceedings IEEE Workshop Multimedia Signal Processing, Shanghai, China, Oct 2005Google Scholar
  8. 8.
    Chang, C.L., Girod, B.: Direction-adaptive discrete wavelet transform via directional lifting and bandeletization. In: Proceedings IEEE International Conference Image Processing, Atlanta, GA, Oct 2006Google Scholar
  9. 9.
    Dong, W, Shi, G. Member, IEEE, and JizhengXu, Member, IEEE,” Adaptive Nonseparable Interpolation for Image Compression With Directional Wavelet Transform”, in IEEE SIGNAL PROCESSING LETTERS, VOL. 15, 2008Google Scholar
  10. 10.
    Kingsbury, N.G., Reeves, T.H.: Redundant representation with complex wavelets: how to achieve sparsity. In: Proceedings International Conference Image Processing, Barcelona, Sept 2003Google Scholar
  11. 11.
    Reeves, T.H., Kingsbury,N.G.: Overcomplete image coding using iterative projection-based noise shaping. In: Proceedings International Conference Image Processing, Rochester, NY, Sept 2002Google Scholar
  12. 12.
    Wang, B., et al.: An investigation of 3D dual-tree wavelet transform for video coding. In: Proceedings International Conference Image Processing, Singapore, Oct 2004Google Scholar
  13. 13.
    Wang, B., et al.: Video coding using 3-D dual-tree wavelet transforms. In: Proceedings International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, Mar 2005Google Scholar
  14. 14.
    Liu, J., Moulin, P.: Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients. IEEE Trans. Image Process. 10(11), 1647–1658 (2001)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Figueras, R.M., Ventura, I., et al.: Low-rate and flexible image coding with redundant representations, IEEE Trans. Image Process. 15(3), Mar 2006Google Scholar
  16. 16.
    Said, A., Pearlman, W.A.: A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Tech. 6, 243–250 (1996)CrossRefGoogle Scholar
  17. 17.
    Taubman, D.: High performance scalable image compression with ebcot. IEEE Trans. Image Process. 9, 1158–1170 (2000)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationSobhasaria Engineering CollegeSikarIndia

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