Advertisement

Empirical Evaluation of Boundary Policies for Wavelet-Based Image Coding

  • Claudia Schremmer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2251)

Abstract

The wavelet transform has become the most interestingn new algorithm for still image compression. Yet there are many parameters within a wavelet analysis and synthesis which govern the quality of a decoded image. In this paper, we discuss different image boundary policies and their implications for the decoded image. A pool of gray-scale images has been wavelet-transformed at different settings of the wavelet filter bank and quantization threshold and with three possible boundary policies.

Our empirical evaluation is based on three benchmarks: a first judgment regards the perceived quality of the decoded image. The compression rate is a second crucial factor. Finally, the best parameter settings with regard to these two factors is weighted with the cost of implementation. Contrary to the JPEG2000 standard, where mirror paddingis implemented, our investigation proposes circular convolution as the boundary treatment.

Keywords

Wavelet Analysis Boundary Policies Empirical Evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Michael D. Adams and Faouzi Kossentini. Performance Evaluation of Reversible Integer-to-Integer Wavelet Transforms for Image Compression. In Proc. IEEE Data Compression Conference, page 514 ff., Snowbird, Utah, March 1999.Google Scholar
  2. 2.
    Ingrid Daubechies. Ten Lectures on Wavelets, volume 61. SIAM. Society for Industrial and Applied Mathematics, Philadelphia, PA, 1992.zbMATHGoogle Scholar
  3. 3.
    Javier Garcia-Frias, Dan Benyamin, and John D. Villasenor. Rate Distortion Optimal Parameter Choice in a Wavelet Image Communication System. In Proc. IEEE International Conference on Image Processing, pages 25–28, Santa Barbara, CA, October 1997.Google Scholar
  4. 4.
    ITU. JPEG2000 Image Coding System. Final Committee Draft Version 1.0-FCD15444-1. International Telecommunication Union, March 2000.Google Scholar
  5. 5.
    Jelena Kovčević and Wim Sweldens. Wavelet Families of Increasing Order in Arbitrary Dimensions. IEEE Trans. on Image Processing, 9(3):480–496, March 2000.Google Scholar
  6. 6.
    Jelena Kovačević and Martin Vetterli. Nonseparable Two-and Three-Dimensional Wavelets. IEEE Trans. on Signal Processing, 43(5):1269–1273, May 1995.Google Scholar
  7. 7.
    Stéphane Mallat. A Wavelet Tour of Signal Processing. Academic Press, San Diego, CA, 1998.zbMATHGoogle Scholar
  8. 8.
    Athanassios N. Skodras, Charilaos A. Christopoulos, and Touradj Ebrahimi. JPEG2000: The Upcoming Still Image Compression Standard. In 11th Portuguese Conference on Pattern Recognition, pages 359–366, Porto, Portugal, May 2000.Google Scholar
  9. 9.
    Tilo Strutz. Untersuchungen zur skalierbaren Kompression von Bildsequenzen bei niedrigen Bitraten unter Verwendung der dyadischen Wavelet-Transformation. PhD thesis, Universität Rostock, Germany, May 1997.Google Scholar
  10. 10.
    John D. Villasenor, Benjamin Belzer, and Judy Liao. Wavelet Filter Evaluation for Image Compression. IEEE Trans. on Image Processing, 2:1053–1060, August 1995.Google Scholar
  11. 11.
    Mladen Victor Wickerhauser. Adapted Wavelet Analysis from Theory to Software. A. K. Peters Ltd., Natick, MA, 1998.Google Scholar
  12. 12.
    Mathias Wien and Claudia Meyer. Adaptive Block Transform for Hybrid Video Coding. In Proc. SPIE Visual Communications and Image Processing, pages 153–162, San Jose, CA, January 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Claudia Schremmer
    • 1
  1. 1.Praktische Informatik IVUniversität MannheimMannheimGermany

Personalised recommendations