Lossless compression of pre-press images using a novel colour decorrelation technique

  • Steven Van Assche
  • Wilfried Philips
  • Ignace Lemahieu
Poster Session C: Compression, Hardware & Software, Image Databases, Neural Networks, Object Recognition & Construction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)


In the pre-press industry colour images have both a high spatial and a high colour resolution. Such images require a considerable amount of storage space and impose long transmission times. Data compression is desired to reduce these storage and transmission problems. Most existing compression schemes operate on gray-scale images. However, in the case of colour images higher compression ratios can be achieved by exploiting inter-colour redundancies.

In this paper a new lossless colour transform is proposed, based on the KLT. This transform removes redundancies in the colour representation of each pixel and can be combined with many existing compression schemes. In this paper it is combined with a prediction scheme that exploits spatial redundancies.

The results proposed in this paper show that the colour transform typically saves about a half to two bit per pixel, compared to a purely predictive scheme. The results also suggest that combining the proposed KLT scheme with the state-of-the-art CALIC gray-scale-only coder could significantly increase the compression ratio of that scheme.


Compression Ratio Prediction Scheme Spatial Prediction Lossless Compression Arithmetic Coder 
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.


  1. 1.
    K. Denecker, J. Van Overloop, and I. Lemahieu. An experimental comparison of several lossless image coders for medical images. In S. Hagerty and R. Renner, editors, Proceedings of the Data Compression Industry Workshop, pages 67–76, Snowbird, Utah, USA, March 1997. Ball Aerospace & Technologies Corp.Google Scholar
  2. 2.
    Koen Denecker and Ignace Lemahieu. Lossless colour image compression using inter-colour error prediction. In J.-P. Veen, editor, Proceedings of the PRORISC IEEE Benelux Workshop on Circuits, Systems and Signal Processing, pages 95–100, Mierlo, Nederland, November 1996. STW Technology Foundation.Google Scholar
  3. 3.
    Steven Dewitte and Jan Cornelis. Lossless integer wavelet transform. IEEE Signal Processing Letters, 1997. To be published.Google Scholar
  4. 4.
    Paul G. Howard and Jeffrey Scott Vitter. Arithmetic coding for data compression. Proceedings of the IEEE, 82(6):857–865, June 1994.CrossRefGoogle Scholar
  5. 5.
    Paul Glor Howard. The Design and Analysis of Efficient Lossless Data Compression Systems. PhD thesis, Department of Computer Science, Brown University, Providence, Rhode Island, June 1993.Google Scholar
  6. 6.
    Nasir D. Memon and Khalid Sayood. Lossless compression of RGB color images. Optical Engineering, 34(6):1711–1717, 1995.Google Scholar
  7. 7.
    W. Philips and K. Denecker. A new embedded lossless/quasi-lossless image coder based on the Hadamard transform. In Proceedings of the IEEE International Conference on Image Processing (ICIP97), 1996. To be published.Google Scholar
  8. 8.
    William K. Pratt. Digital image processing. New-York: Wiley-Interscience, second edition, 1991.Google Scholar
  9. 9.
    John A. Robinson. Efficient general-purpose image compression with binary tree predictive coding. IEEE Transactions on Image Processing, 6(4):601–608, April 1997.CrossRefGoogle Scholar
  10. 10.
    Amir Said and William A. Pearlman. Image compression via multiresolution representation and predictive coding. In Visual Communications and Image Processing, number 2094 in SPIE, pages 664–674, November 1993.Google Scholar
  11. 11.
    Amir Said and William A. Pearlman. An image multiresolution representation for lossless and lossy compression. In SPIE Symposium on Visual Communications and Image Processing, Cambridge, MA, November 1993.Google Scholar
  12. 12.
    The International Telegraph and Telephone Consultative Committee (CCITT), editors. Digital Compression and Coding of Continuous-Tone Still Images. Recommendation T.81, 1992.Google Scholar
  13. 13.
    Xiaolin Wu and Nasir Memon. CALIC — a context based adaptive lossless image codec. In IEEE International Conference on Acoustics, Speech, & Signal Pocessing, volume 4, pages 1890–1893, May 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Steven Van Assche
    • 1
  • Wilfried Philips
    • 1
  • Ignace Lemahieu
    • 1
  1. 1.Department of Electronics and Information SystemsUniversity of GhentGentBelgium

Personalised recommendations