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Lossless Compression of Medical and Natural High Bit Depth Sparse Histogram Images

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Book cover Beyond Databases, Architectures and Structures (BDAS 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 521))

Abstract

In this paper we overview histogram packing methods and focus on an off-line packing method, which requires encoding the original histogram along with the compressed image. For a diverse set containing medical MR, CR and CT images as well as various natural 16-bit images, we report histogram packing effects obtained for several histogram encoding methods. The histogram packing improves significantly JPEG2000 and JPEG-LS lossless compression ratios of high bit depth sparse histogram images. In case of certain medical image modalities the improvement may exceed a factor of two, which indicates that histogram packing should be exploited in medical image databases as well as in medical picture archiving and communication systems in general as it is both highly advantageous and easy to apply.

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References

  1. Ausbeck Jr., P.J.: The piecewice-constant image model. Proc. of the IEEE 88(11), 1779–1789 (2000)

    Article  Google Scholar 

  2. Bernas, T., Starosolski, R., Robinson, J.P., Rajwa, B.: Application of detector precision characteristics and histogram packing for compression of biological fluorescence micrographs. Computer Methods and Programs in Biomedicine 108(2), 511–523 (2012), http://dx.doi.org/10.1016/j.cmpb.2011.03.012

    Article  Google Scholar 

  3. Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system an overview. IEEE Trans. on Consumer Electronics 46(4), 1103–1127 (2000)

    Article  Google Scholar 

  4. Ferreira, P.J., Pinho, A.J.: Why does histogram packing improve lossless compression rates? IEEE Signal Processing Letters 9(8), 259–261 (2002)

    Article  Google Scholar 

  5. INCITS/ISO/IEC, ITU-T: Information technology – JPEG 2000 image coding system: Core coding system, INCITS/ISO/IEC International Standard 15444-1 and ITU-T Recommendation T.800 (2004)

    Google Scholar 

  6. INCITS/ISO/IEC, ITU-T: Information technology – JPEG 2000 image coding system: Extensions, INCITS/ISO/IEC International Standard 15444-2 and ITU-T Recommendation T.801 (2008)

    Google Scholar 

  7. INCITS/ISO/IEC, ITU-T: Information technology – Lossless and near-lossless compression of continuous-tone still images – Baseline, INCITS/ISO/IEC International Standard 14495-1 and ITU-T Recommendation T.87 (2006)

    Google Scholar 

  8. INCITS/ISO/IEC, ITU-T: Information technology – Lossless and near-lossless compression of continuous-tone still images: Extensions, INCITS/ISO/IEC International Standard 14495-2 and ITU-T Recommendation T.870 (2003)

    Google Scholar 

  9. Iwahashi, M., Kobayashi, H., Kiya, H.: Lossy compression of sparse histogram image. In: Proc. ICASSP 2012, pp. 1361–1364 (2012), http://dx.doi.org/10.1109/ICASSP.2012.6288143

  10. Nasr-Esfahani, E., Samavi, S., Karimi, N., Shiran, S.: Near lossless image compression by local packing of histogram. In: Proc. ICASSP 2008, pp. 1197–1200 (2008), http://dx.doi.org/10.1109/ICASSP.2008.4517830

  11. NEMA: Digital imaging and communications in medicine (DICOM) part 5: Data structures and encoding, National Electrical Manufacturers Association Standard DICOM PS3.5 2014c (2014)

    Google Scholar 

  12. Pinho, A.J.: On the impact of histogram sparseness on some lossless image compression techniques. In: Proc. ICIP 2001, vol. II, pp. 442–445 (2001)

    Google Scholar 

  13. Pinho, A.J.: A comparison of methods for improving the lossless compression of images with sparse histograms. In: Proc. ICIP 2002, vol. 2, pp. 673–676 (2002)

    Google Scholar 

  14. Pinho, A.J.: An online preprocessing technique for improving the lossless compression of images with sparse histograms. IEEE Signal Processing Letters 9(1), 5–7 (2002)

    Article  Google Scholar 

  15. Pinho, A.J.: Preprocessing techniques for improving the lossless compression of images with quasi-sparse and locally sparse histograms. In: Proc. ICME 2002, vol. 1, pp. 633–636 (2002)

    Google Scholar 

  16. Starosolski, R.: Compressing images of sparse histograms. In: Kowalczyk, A., Fercher, A.F., Tuchin, V.V. (eds.) Proc. SPIE: Medical Imaging, vol. 5959, pp. 209–217 CID:595912. The International Society for Optical Engineering, Bellingham (2005), http://dx.doi.org/10.1117/12.624489

  17. Starosolski, R.: Performance evaluation of lossless medical and natural continuous tone image compression algorithms. In: Kowalczyk, A., Fercher, A.F., Tuchin, V.V. (eds.) Proc. SPIE: Medical Imaging, vol. 5959, pp. 116–127 CID:59590L. The International Society for Optical Engineering, Bellingham (2005), http://dx.doi.org/10.1117/12.624136

  18. Starosolski, R.: Compressing high bit depth images of sparse histograms. In: Simos, T.E., Psihoyios, G. (eds.) International Electronic Conference on Computer Science, AIP Conference Proceedings, vol. 1060, pp. 269–272. American Institute of Physics, USA (2008), http://dx.doi.org/10.1063/1.3037069

    Google Scholar 

  19. Starosolski, R.: New simple and efficient color space transformations for lossless image compression. Journal of Visual Communication and Image Representation 25(5), 1056–1063 (2014), http://dx.doi.org/10.1016/j.jvcir.2014.03.003

    Article  Google Scholar 

  20. W3C, ISO/IEC: Information technology – Computer graphics and image processing – Portable Network Graphics (PNG): Functional specification, World Wide Web Consortium Recommendation and ISO/IEC International Standard 15948 (2004)

    Google Scholar 

  21. Weinberger, M.J., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Proc. 9(8), 1309–1324 (2000)

    Article  Google Scholar 

  22. Wu, X., Memon, N.: Context-based, adaptive, lossless image codec. IEEE Trans. on Communications 45(4), 437–444 (1997)

    Article  Google Scholar 

  23. Yoo, Y., Kwon, Y.G., Ortega, A.: Embedded image-domain compression of simple images. In: Proc. of the 32nd Asilomar Conf. on Signals, Systems, and Computers, vol. 2, pp. 1256–1260 (1998)

    Google Scholar 

  24. Yoo, Y., Kwon, Y.G., Ortega, A.: Embedded image domain compression using context models. In: Proc. ICIP 1999, pp. 477–481 (1999)

    Google Scholar 

  25. Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Trans. on Information Theory 32(3), 337–343 (1997)

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Roman Starosolski

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  1. Roman Starosolski
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Correspondence to Roman Starosolski .

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Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Silesian University of Technology, Gliwice,, Poland

    Daniel Kostrzewa

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Starosolski, R. (2015). Lossless Compression of Medical and Natural High Bit Depth Sparse Histogram Images. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-319-18422-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-18422-7_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18421-0

  • Online ISBN: 978-3-319-18422-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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