Advertisement

Image Compression by Approximated 2D Karhunen Loeve Transform

  • Władysław Skarbek
  • Adam Pietrowcew
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)

Abstract

Image compression is performed by 8 x 8 block transform based on approximated 2D Karhunen Loeve Transform. The transform matrix W is produced by eight pass, modified Oja-RLS neural algorithm which uses the learning vectors creating the image domain subdivision into 8 x 1 blocks. In transform domain, the stages of quantisation and entropy coding follow exactly JPEG standard principles. It appears that for images of natural scenes, the new scheme outperforms significantly JPEG standard: at the same bitrates it gives up to two decibels increase of PSNR measure while at the same image quality it gives up to 50% lower bitrates. Despite the time complexity of the proposed scheme is higher than JPEG time complexity, it is practical method for handling still images, as C++ implementation on PC platform, can encode and decode for instance LENA image in less than two seconds.

Keywords

Discrete Cosine Transform Image Compression Quantisation Scale Entropy Code Quantisation Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bannour S., Azimi-Sadjadi M.R.: Principal component extraction using recursive least squares learning. IEEE Transactions on Neural Networks. 6 (1995) 457–469CrossRefGoogle Scholar
  2. 2.
    Cichocki A., Kasprzak W., Skarbek W.: Adaptive learning algorithm for Principal Component Analysis with partial data. Proceedings of 13-th European Meeting on Cybernetics and Systems Research. Austrian Society for Cybernetics Studies, Vienna, Austria (1996) 1014–1019Google Scholar
  3. 3.
    Diamantaras K. I., Kung S. Y.: Principal component neural networks — Theory and applications. John Wiley & Sons, Inc. (1995)Google Scholar
  4. 4.
    Jain A.K.: Fundamentals of digital image processing. Prentice-Hall International, Englewood Cliffs NJ (1994)Google Scholar
  5. 5.
    Mitchell, Pennebaker: The JPEG standard (1995)Google Scholar
  6. 6.
    Oja, E.: A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology. 15 (1982) 267–273zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Sikora, R., Skarbek, W.: On stability of Oja-RLS algorithm. Fundamenta Informaticae. 34 (1998) 441–453zbMATHMathSciNetGoogle Scholar
  8. 8.
    Skarbek, W., Sikora, R., Pietrowcew, A.: Modified Oja-RLS Algorithm — Stochastic Convergence Analysis and Application for Image Compression. Fundamenta Informaticae. (to appear in 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Władysław Skarbek
    • 1
  • Adam Pietrowcew
    • 2
  1. 1.Department of Electronics and Information TechnologyWarsaw University of TechnologyWarszawaPoland
  2. 2.Department of InformaticsTechnical University of BialystokBialystok

Personalised recommendations