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Lossless Compression Using Joint Predictor for Astronomical Images

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Book cover Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5876))

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Abstract

Downloading astronomical images through Internet is a slow operation due to their huge size. Although several lossless image coding standards that have good performance have been developed in the past years, none of them are specifically designed for astronomical data. Motivated by this, this paper proposes a lossless coding scheme for astronomical image compressions. We design a joint predictor which combines the interpolation predictor and partial MMSE predictor. Such strategy benefits from its high compression ratio and low computation complexity. Moreover, the scalable and embedding functions can be further supported. The interpolation predictor is realized by upsampling the downsampled input image using bi-cubic interpolation, while the partial minimum mean square error (MMSE) predictor predicts the background and foreground (i.e., stars) separately. Finally, we design a simplified Tier-1 coder from JPEG2000 for entropy coding. Our experimental results show that the proposed encoder can achieve higher compression ratio than JPEG2000 and JPEG-LS.

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Author information

Authors and Affiliations

  1. Visual Communications Laboratory, Department of Communication Engineering, National Central University, Jhongli, Taiwan

    Bo-Zong Wu & Angela Chih-Wei Tang

Authors
  1. Bo-Zong Wu
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  2. Angela Chih-Wei Tang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, 338-8570, Japan

    Yoshinori Kuno

  6. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  7. Univ. of Zurich, Department of Informatics, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  8. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  9. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  10.  ,  

    Miguel L. Encarnação

  11. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  12. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Wu, BZ., Tang, A.CW. (2009). Lossless Compression Using Joint Predictor for Astronomical Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-10520-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10519-7

  • Online ISBN: 978-3-642-10520-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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