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Experimental Astronomy

, Volume 39, Issue 3, pp 495–512 | Cite as

Astronomical context coder for image compression

  • Petr PataEmail author
  • Jaromir Schindler
Article

Abstract

Recent lossless still image compression formats are powerful tools for compression of all kind of common images (pictures, text, schemes, etc.). Generally, the performance of a compression algorithm depends on its ability to anticipate the image function of the processed image. In other words, a compression algorithm to be successful, it has to take perfectly the advantage of coded image properties. Astronomical data form a special class of images and they have, among general image properties, also some specific characteristics which are unique. If a new coder is able to correctly use the knowledge of these special properties it should lead to its superior performance on this specific class of images at least in terms of the compression ratio. In this work, the novel lossless astronomical image data compression method will be presented. The achievable compression ratio of this new coder will be compared to theoretical lossless compression limit and also to the recent compression standards of the astronomy and general multimedia.

Keywords

Image compression in astronomy Lossless technique Image context model RICE HCOMPRESS JPEG2000 

Notes

Acknowledgments

The work has been supported by the grant No. 14-25251S “Nonlinear imaging systems with spatially variant point spread function” of the Czech Science Foundation.

References

  1. 1.
    Anisimova, E., Bednar, J., Blazek, M., Janout, P., Fliegel, K., et al.: Estimation and measurement of space-variant features of imaging systems and influence of this knowledge on accuracy of astronomical measurement. In Applications of Digital Image Processing XXXVII. Bellingham: SPIE, art. no. 92171E, p. 92171E-1-92171E-13 (2014)Google Scholar
  2. 2.
    Anisimova, E., Fliegel, K., Blazek, M., Janout, P., Bednar, J., et al.: Analysis of images obtained from space-variant astronomical imaging systems. In Applications of Digital Image Processing XXXVI. Bellingham (stát Washington): SPIE, art. no. 7, p. 885607-1-885607-11 (2013)Google Scholar
  3. 3.
    Banon, G.J.F., Barrera, J., Braganeto, U.M.: Mathematical morphology and its application to signal and image processing. Proceeding of the 8th international symposium on mathematical morphology, ISBN 978-85-17-00032-4 (2007)Google Scholar
  4. 4.
    Bloomfield, V.A.: Using R for Numerical Analysis in Science and Engineering. Chapman & Hall/CRC, Boca Raton: CRC Press, ISBN 978143-9-884485 (2014)Google Scholar
  5. 5.
    Castro Tirado, A.J., Bernas, M., Rezek, M., Soldan, J., Pata, P., et al.: The Burst Observer and Optical Transient Exploring System (BOOTES). In Astronomy and Astrophysics: Gamma-Ray Bursts in the Afterglow Era. Les Ulis: EDP Sciences, p. 583–585 (1999)Google Scholar
  6. 6.
    Castro-Tirado, A.J., Sánchez Moreno, F.M., Pérez del Pulgar, C., Azócar, D., Beskin, G., et al.: The GLObal Robotic telescopes Intelligent Array for E-Science (GLORIA). In III Workshop on Robotic Autonomous Observatories. México: Universidad Nacional Autónoma de México, p. 104–109 (2014)Google Scholar
  7. 7.
    CCSDS: Lossless Data Compression, Recommendation for space data system standards, CCSDS, Vol. 121.0-B-1 (1997)Google Scholar
  8. 8.
    GLORIA team: GLObal Robotic-telescopes Intelligent Array, [online], http://gloria-project.eu/en/ (2015)
  9. 9.
    ISO/IEC 15444-1:2000: JPEG2000 Image Coding System (core coding system), [online], http://www.jpeg.org/FCD15444-1.htm (2000)
  10. 10.
    Izenman, A.J.: Recent developments in nonparametric density estimation. J. Am. Stat. Assoc. 86(413), 205–224 (1991)zbMATHMathSciNetGoogle Scholar
  11. 11.
    Janout, P., Pata, P., Bednar, J., Anisimova, E., Blazek, M., et al.: Stellar objects identification using wide-field camera. In Proc. SPIE 9450, Photonics, Devices, and Systems VI. Bellingham: SPIE, art. no. 94501I, p. 94501I-1-94501I-9 (2015)Google Scholar
  12. 12.
    Jelinek, M., Kubanek, P., Hudec, R., Nekola, M., Topinka, M., Strobl J.: BART - Burst alert robotic telescope, the astrophysics of cataclysmic variables and related objects. Proceedings of ASP Conference Vol. 330. Edited by J.-M. Hameury and J.-P. Lasota. San Francisco: Astronomical Society of the Pacific, p. 481 (2005)Google Scholar
  13. 13.
    Klima, M., Fliegel, K., Pata, P., Vitek, S., Blazek, M., et al.: DEIMOS - an open source image database. Radioengineering 20(4), 1016–1023 (2011)Google Scholar
  14. 14.
    Koenker, R., d’ Orey, V.: Remark AS R92: a remark on algorithm AS 229: Computing dual regression quantiles and regression rank scores. Applied Statistics, Blackwell Publishers, 410–414 (1994)Google Scholar
  15. 15.
    Koenker, R.: Quantile Regression, [online], http://cran.r-project.org/web/packages/quantreg/quantreg.pdf (2014)
  16. 16.
    Koten, P., Fliegel, K., Vitek, S., Pata, P.: Automatic video system for continues monitoring of the meteor activity. Earth Moon Planet. 108(1), 69–76 (2011)CrossRefADSGoogle Scholar
  17. 17.
    Manders, C., Farbiz, F., Mann, S.: A compression method for arbitrary precision floating-point images, image processing. ICIP 2007. IEEE International Conference on, vol.4, no., pp.IV - 165,IV - 168, Sept. 16 2007–Oct. 19 2007 (2007)Google Scholar
  18. 18.
    Official JPEG homepage, [online], http://www.jpeg.org (2015)
  19. 19.
    Pata, P.: Influence of the lossy compression JPEG2000 standard on the deformation of PSF. Acta Polytech. 51(6), 54–56 (2011)Google Scholar
  20. 20.
    Pavlov, I.: 7-ZIP, [online], http://www.7-zip.org/ (2015)
  21. 21.
    Pence, W., Seaman, R., White, R.: Lossless astronomical image compression and the effects of noise. Astron. Soc. Pac. 121(878), 414–427 (2009)CrossRefADSGoogle Scholar
  22. 22.
    Pence, W.D., Seaman, R., White, R.L.: Fpack FITS image compression utility, [online], http://heasarc.gsfc.nasa.gov/fitsio/fpack/fpackguide.pdf (2010)
  23. 23.
    Pinheiro, A., Fliegel, K., Korshunov, P., Krasula, L., Bernardo, M., et al.: Performance evaluation of the emerging JPEG XT image compression standard. In 2014 I.E. 16th International Workshop on Multimedia Signal Processing (MMSP). Piscataway: IEEE (2014)Google Scholar
  24. 24.
    Portnoy, S., Koenker, R.: The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. Stat. Sci. Inst. Math. Stat. 12(4), 279–300 (1997)zbMATHMathSciNetGoogle Scholar
  25. 25.
    Rice, R.F., Yeh, P.-S., Miller, W.: Algorithms for a very high speed universal noiseless coding module. JPL Publication 91-1, Jet Propulsion Laboratory, Pasadena, CA (1991)Google Scholar
  26. 26.
    Schindler, J.: Astronomical image data compression, doctoral thesis, CTU in Prague (2010)Google Scholar
  27. 27.
    Schindler, J., Pata, P.: Spatially Adaptive DWT for image compression. In Photonics, Devices, and Systems III. Bellingham: SPIE, p. 21-1-21-6 (2006)Google Scholar
  28. 28.
    Seaman, R., Pence, W., White, R.: Astronomical tiled image compression: how & why. Astronomical Data Analysis Software and Systems XVI. 30 (2006)Google Scholar
  29. 29.
    Shamir, L., Nemiroff, R.J.: PHOTZIP: a lossy FITS image compression algorithm that protects user-defined levels of photometric integrity. Astron. J. 129, 539–546 (2005)CrossRefADSGoogle Scholar
  30. 30.
    Starck, J.L., Murtagh, F., Louys, M.: Astronomical image compression using the pyramidal median transform. Astronomical Data Analysis SOftware and Systems IV, ASP Conference Series. 77 (1995)Google Scholar
  31. 31.
    Svihlik, J., Fliegel, K., Koten, P., Vitek, P.: Pata: noise analysis of MAIA system and possible noise suppression. Radioengineering 20(1), 110–117 (2011)Google Scholar
  32. 32.
    Svihlik, J., Fliegel, K., Kukal, J., Jehotova, E., Pata, P., et al.: Estimation of non-Gaussian noise parameters in the wavelet domain using the moment-generating function. J. Electron. Imaging. 21(2): art. no. 023025, p. 023025-1-023025-15 (2012)Google Scholar
  33. 33.
    Svihlik, J., Pata, P.: Elimination of thermally generated charge in charged coupled devices using Bayesian estimator. Radioengineering 17(2), 119–124 (2008)Google Scholar
  34. 34.
    Véran, J.P., Wright, J.R.: Compression software for astronomical images. Astronomical Data Analysis Software and Systems III. 61 (1994)Google Scholar
  35. 35.
    W. H. Press: Wavelet-Based Compression Software for FITS Images, Astronomical Data Analysis Software and Systems I. 25 (1992)Google Scholar
  36. 36.
    White, R., Postman, M., Lattanzi, M.: Digitized Optical Sky Survey, pp. 167–175. Kluwer, Dordrecht (1992)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringCzech Technical University in PraguePrague 6Czech Republic

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