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German Conference on Pattern Recognition

DAGM 2015: Pattern Recognition pp 343–355Cite as

High Speed Lossless Image Compression

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

Abstract

We introduce a simple approach to lossless image compression, which makes use of SIMD vectorization at every processing step to provide very high speed on modern CPUs. This is achieved by basing the compression on delta coding for prediction and bit packing for the actual compression, allowing a tuneable tradeoff between efficiency and speed, via the block size used for bit packing. The maximum achievable speed surpasses main memory bandwidth on the tested CPU, as well as the speed of all previous methods that achieve at least the same coding efficiency.

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Acknowledgements

This research was financially supported by the Juniorprofessorenprogramm Baden-Württemberg.

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Authors and Affiliations

  1. University of Stuttgart, Stuttgart, Germany

    Hendrik Siedelmann, Alexander Wender & Martin Fuchs

  2. Heidelberg University, Heidelberg, Germany

    Hendrik Siedelmann

Authors
  1. Hendrik Siedelmann
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  2. Alexander Wender
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  3. Martin Fuchs
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Corresponding author

Correspondence to Hendrik Siedelmann .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Siedelmann, H., Wender, A., Fuchs, M. (2015). High Speed Lossless Image Compression. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_28

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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