EZW-Based Image Compression with Omission and Restoration of Wavelet Subbands

  • Francisco A. Pujol
  • Higinio Mora
  • José Luis Sánchez
  • Antonio Jimeno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


It is well known that multimedia applications provide the user with information through different methods (text, data, graphics, images, audio, video, etc.) which must be digitally represented, transmitted, stored and processed. Due to the fact that there is an increasing interest in developing high definition systems, multimedia applications are demanding, among others, higher bandwidth resources and more memory requirements in embedded devices. Therefore, it is essential to use compression techniques to reduce the time requirements of these new applications. This work aims to design an EZW-based image compression model, which makes use of the omission and restoration of wavelet subbands, providing high compression rates, good quality standards and low computation time requirements. The results obtained show that our method satisfies these assumptions and can be integrated in new multimedia devices.


Image Compression Wavelet Transform EZW Algorithm 


  1. 1.
    Usevitch, B.E.: A Tutorial on Modern Lossy Wavelet Image Compression: Foundations of JPEG 2000. IEEE Signal Processing Magazine 18, 22–35 (2001)CrossRefGoogle Scholar
  2. 2.
    Torres, L., Delp, E.J.: New Trends in Image and Video Compression. In: Proc. of the European Signal Processing Conference (2000)Google Scholar
  3. 3.
    Shapiro, J.M.: Embedded Image Coding using Zerotrees of Wavelet Coefficients. IEEE Transactions on Signal Processing 41, 3445–3462 (1993)zbMATHCrossRefGoogle Scholar
  4. 4.
    Adams, M.D., Antoniou, A.: Reversible EZW-based Image Compression Using Best-Transform Selection and Selective Partial Embedding. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 47, 1119–1122 (2000)CrossRefGoogle Scholar
  5. 5.
    Danyali, H., Mertins, A.: Flexible, Highly Scalable, Object-based Wavelet Image Compression Algorithm for Network Applications. IEE Proceedings Vision, Image and Signal Processing 151, 498–510 (2004)CrossRefGoogle Scholar
  6. 6.
    Taubman, D.: High Performance Scalable Image Compression with EBCOT. IEEE Transactions on Image Processing 9, 1158–1170 (2000)CrossRefGoogle Scholar
  7. 7.
    Sudhakar, R., Karthiga, R., Jayaraman, S.: Image Compression using Coding of Wavelet Coefficients – A Survey. ICGST International Journal on Graphics, Vision and Image Processing 5, 25–38 (2005)Google Scholar
  8. 8.
    Saha, S.: Image Compression - From DCT to Wavelets: A Review. ACM Crossroads 6, 12–21 (2000)CrossRefGoogle Scholar
  9. 9.
    Truchetet, F., Laligant, O.: Wavelets in Industrial Applications: a Review. In: Proc. of the SPIE: Wavelet Applications in Industrial Processing II, pp. 1–14 (2004)Google Scholar
  10. 10.
    Bovik, A.: Handbook of Image and Video Processing. Academic Press, London (2000)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Francisco A. Pujol
    • 1
  • Higinio Mora
    • 1
  • José Luis Sánchez
    • 1
  • Antonio Jimeno
    • 1
  1. 1.Specialized Processor Architectures Lab, Dept. Tecnología Informática y Computación, Universidad de Alicante, P.O. Box 99, E-03080 AlicanteSpain

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