Skip to main content
SpringerLink
Log in

An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a new approach for a block-based lossless image compression using finite mixture models and adaptive arithmetic coding. Conventional arithmetic encoders encode and decode images sample-by-sample in raster scan order. In addition, conventional arithmetic coding models provide the probability distribution for whole source symbols to be compressed or transmitted, including static and adaptive models. However, in the proposed scheme, an image is divided into non-overlapping blocks and then each block is encoded separately by using arithmetic coding. The proposed model provides a probability distribution for each block which is modeled by a mixture of non-parametric distributions by exploiting the high correlation between neighboring blocks. The Expectation-Maximization algorithm is used to find the maximum likelihood mixture parameters in order to maximize the arithmetic coding compression efficiency. The results of comparative experiments show that we provide significant improvements over the state-of-the-art lossless image compression standards and algorithms. In addition, experimental results show that the proposed compression algorithm beats JPEG-LS by 9.7 % when switching between pixel and prediction error domains.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. http://links.uwaterloo.ca/repository.html

References

  1. Abramson N (1963) Information theory and coding. McGraw-Hill Book Company, Inc., New York

    Google Scholar 

  2. Alcaraz-Corona S, Rodriguez-Dagnino RM (2010) Bi-level image compression estimating the markov order of dependencies. IEEE J Sel Topics Signal Process 4(3):605–611

    Article  Google Scholar 

  3. Auli-Llinas F (2011) Stationary probability model for bitplane image coding through local average of wavelet coefficients. IEEE Trans Image Process 20(8):2153–2165

    Article  MathSciNet  Google Scholar 

  4. Burrows M, Wheeler DJ (1994) A block-sorting lossless data compression algorithm, Tech. Rep., digital equipment corporation, Palo Alto, California

  5. Carpentieri B (1997) A new lossless image compression algorithm based on arithmetic coding. In: Proceedings of the 9th international conference on image analysis and processing-volume II, pp 54–61

  6. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc Ser B 39(1):1–38

    MathSciNet  MATH  Google Scholar 

  7. Howard PG, Vitter JS (1993) Fast and efficient lossless image compression. In: Proceedings 1993 data compression conference. Mars, IEEE Computer Society Press, pp 351–360

  8. Howard PG, Vitter JS (1994) Arithmetic Coding for Data Compression. Proc IEEE 82(6):857–865

    Article  Google Scholar 

  9. Jeff Wu CF (1983) On the convergence properties of the EM algorithm. Ann Stat 11(1):95–103

    Article  MATH  Google Scholar 

  10. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:49–86

    Article  MathSciNet  Google Scholar 

  11. Kuroki N, Manabe T, Numa M (2004) Adaptive arithmetic coding for image prediction errors. In: Proceedings of IEEE international symposium on circuits and systems (ISCAS 04), vol III, pp 961–964

  12. Langdon GG (1984) An Introduction to Arithmetic Coding. IBM J Res Dev 28:2

    Article  Google Scholar 

  13. Masmoudi A, Masmoudi A (2013) A new arithmetic coding model for a block-based lossless image compression based on exploiting inter-block correlation. Sig Image Video Process 1–7

  14. Masmoudi A, Puech W, Bouhlel MS (2010) Efficient adaptive arithmetic coding based on updated probability distribution for lossless image compression. J Electron Imaging 19(2):023014

    Article  Google Scholar 

  15. Matsuda I, Shirai N, Itoh S (2003) Lossless coding using predictors and arithmetic code optimized for each image. In: Proceedings of international workshop visual content processing and representation, vol 2849, pp 199–207

  16. Mclachlan GP, Peel D (2000) Finite mixture models, wiley series in probability and statistics. Wiley-Interscience, 1st edn

  17. Minoo K, Nguyen T Q (2009) Entropy coding via parametric source model with applications in fast and efficient compression of image and video data. In: DCC’09

  18. Moffat A, Neal RM, Witten IH (1998) Arithmetic coding revisited. ACM Trans Inf Syst 16(3):256–294

    Article  Google Scholar 

  19. Rubin F (1979) Arithmetic stream coding using fixed precision registers. In: Data compression conference, pp 672–675. IEEE transaction on information theory

  20. Salomon D (2007) Data compression the complete reference fourth edition. Springer

  21. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Article  MathSciNet  MATH  Google Scholar 

  22. Taubman DS, Marcellin MW (2002) JPEG2000: image compression fundamentals; standards and practice. Springer

  23. Teuhola J (2011) Nonblock coding of binary sources by probabilistic enumeration. IEEE Trans Inf Theory 57(9):6170–6179

    Article  MathSciNet  Google Scholar 

  24. Weinberger MJ, Seroussi G, Sapiro G (2000) The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Trans Image Process 9(8):1309–1324

    Article  Google Scholar 

  25. Witten IH, Neal RM, Cleary JG (1987) Arithmetic coding for data compression. Commun ACM 30(6):520–540

    Article  Google Scholar 

  26. Wu X, Memon N (1996) CALIC - context based adaptive lossless image codec. IEEE Int Conf Acoust Speech Sig Process 4:1890-1893

    Google Scholar 

  27. Wu J, Xu Z, Jeon G, Zhang X, Jiao L (2013) Arithmetic coding for image compression with adaptive weight-context classification. Sig Process Image Commun 28(7):727–735

    Article  Google Scholar 

  28. Ye H, Deng G, Devlin JC (2002) Parametric probability models for lossless coding of natural images. In EUSIPCO, pp 514–517

  29. Zhang L, Wang D, Zheng D (2012) Segmentation of source symbols for adaptive arithmetic coding. IEEE Trans Broadcast 58(2):228–235

    Article  Google Scholar 

  30. Zhao DY, Samuelsson J, Nilsson M (2008) On entropy-constrained vector quantization using gaussian mixture models. IEEE Trans Commun 56(12):2094–2104

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sfax Preparatory Engineering Institute, University of Sfax, BP 1172-3018, Sfax, Tunisia

    Atef Masmoudi & Afif Masmoudi

  2. LIRMM, UMR CNRS 5506, University of Montpellier II, 34392, Montpellier Cedex 05, France

    Atef Masmoudi & William Puech

Authors
  1. Atef Masmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Puech
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Afif Masmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William Puech.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masmoudi, A., Puech, W. & Masmoudi, A. An improved lossless image compression based arithmetic coding using mixture of non-parametric distributions. Multimed Tools Appl 74, 10605–10619 (2015). https://doi.org/10.1007/s11042-014-2195-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2195-8

Keywords

Search

Navigation