Skip to main content

New Method for Adaptive Lossless Compression of Still Images Based on the Histogram Statistics

  • Conference paper
Intelligent Interactive Multimedia Systems and Services

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 11))

Abstract

The method for adaptive lossless image coding is aimed at efficient compression of grayscale or color digital still images. The method is based on the analysis of the processed images histograms, followed by modified Huffman and Run-length coding. The coding is performed in two consecutive stages. In the first one, the input data (i.e., the digital information about the image brightness and color components) is transformed without affecting their volume in such a way, that to obtain sequences of same values (in particular, zeros) of maximum length. The transform is reversible and is based on the analysis of the input data histograms and on the differences between each number in the processed data and the most frequent one. In the second stage of the processing the transformed data is analyzed and sequences of same numbers are detected. Every such sequence is substituted by a shorter one, corresponding to the number of same values which it contains. The method is extremely suitable for images of graphics or texts (for example: fingerprints, contour images, cartoons, medical signals, etc.). The method has low computational complexity and is suitable for real-time applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ziv, J., Lempel, A.: Compression of individual sequences via variable rate coding. IEEE Trans. on Information Theory IT-24(5), 530–535 (1978)

    Article  MathSciNet  Google Scholar 

  2. Burrows, M., Wheeler, D.: A block-sorting Lossless Data Compression Algorithm. Digital Systems Research Center. SRC Report 124 (1994)

    Google Scholar 

  3. Fano, R.: Transmission of Information. MIT Press, Cambridge (1949)

    Google Scholar 

  4. Huffman, D.: A Method for the Construction of Minimum-Redundancy Codes. Proceedings of the IRE 40(9), 1098–1101 (1952)

    Article  Google Scholar 

  5. Golomb, W.S.: Run-Length Encodings. IEEE Trans. on Information Theory IT-12(3), 399–401 (1966)

    Article  MathSciNet  Google Scholar 

  6. Witten, I., Neal, R., Cleary, J.: Arithmetic Coding for Data Compression. Communications of the ACM 30(6) (1987)

    Google Scholar 

  7. Raita, T., Teuhola, J.: Predictive text compression by hashing. In: ACM Conf. on Information Retrieval (1987)

    Google Scholar 

  8. Karam, L.: Lossless Image Compression. In: Al Bovik (ed.) The Essential Guide to Image Processing, ch. 16, pp. 385–420. Academic Press, London (2009)

    Google Scholar 

  9. Salomon, D.: Data compression. Springer, New York (2004)

    MATH  Google Scholar 

  10. Bell, T., Witten, I., Cleary, J.: Text Compression. Prentice-Hall, Englewood Cliffs (1990)

    Google Scholar 

  11. Cappellini, V.: Data Compression and Error Control Techniques with Applications. Academic Press, New York (1985)

    Google Scholar 

  12. Sayood, K.: Lossless Compression Handbook. Academic Press, London (2002)

    Google Scholar 

  13. Storer, A., Helfgott, H.: Lossless Image Compression by Block Matching. The Computer Journal 40(2/3), 137–145 (1997)

    Article  Google Scholar 

  14. Taubman, D., Marcellin, M.: JPEG 2000 Image Compression Fundamentals, Standards and Practice. Kluwer Academic, Norwell (2002)

    Google Scholar 

  15. Weinberger, M., Seroussi, G., Sapiro, G.: LOCO-I: A Low Complexity, Context-Based, Lossless Image Compression Algorithm. In: Storer, J. (ed.) Proceedings of Data Compression Conference, pp. 140–149. IEEE Computer Society Press, Los Alamitos (1996)

    Google Scholar 

  16. Milanova, M., Kountchev, R.: New Method for Lossless Compression of Medical Records. In: 8th IEEE Intern. Symp. on Signal Processing and In-formation Technology (ISSPIT 2008), Bosnia and Herzegovina, pp. 23–28 (2008)

    Google Scholar 

  17. Kountchev, R., Todorov, V., Kountcheva, R.: Efficient archiving of documents with pro-tection of their authentic contents. Int. J. of Reasoning-based Intelligent Systems (IJRIS) 1(1/2), 43–55 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kountchev, R., Todorov, V., Kountcheva, R. (2011). New Method for Adaptive Lossless Compression of Still Images Based on the Histogram Statistics. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C., Howlett, R.J. (eds) Intelligent Interactive Multimedia Systems and Services. Smart Innovation, Systems and Technologies, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22158-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22158-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22157-6

  • Online ISBN: 978-3-642-22158-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics