Skip to main content

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

Abstract

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.

References

  1. 1.

    Xiong Z, Liveris AD, Cheng S: Distributed source coding for sensor networks. IEEE Signal Processing Magazine 2004,21(5):80-94. 10.1109/MSP.2004.1328091

    Article  Google Scholar 

  2. 2.

    Girod B, Aaron AM, Rane S, Rebollo-Monedero D: Distributed video coding. Proceedings of the IEEE 2005,93(1):71-83.

    Article  Google Scholar 

  3. 3.

    Slepian D, Wolf JK: Noiseless coding of correlated information sources. IEEE Transactions on Information Theory 1973,19(4):471-480. 10.1109/TIT.1973.1055037

    MathSciNet  Article  Google Scholar 

  4. 4.

    Wyner AD, Ziv J: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 1976,22(1):1-10. 10.1109/TIT.1976.1055508

    MathSciNet  Article  Google Scholar 

  5. 5.

    Cover TM, Thomas JA: Elements of Information Theory. John Wiley & Sons, New York, NY, USA; 1991.

    Google Scholar 

  6. 6.

    Nonnis A, Grangetto M, Magli E, Olmo G, Barni M: Improved low-complexity intraband lossless compression of hyperspectral images by means of Slepian-Wolf coding. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 1: 829–832.

    Google Scholar 

  7. 7.

    Barni M, Papini D, Abrardo A, Magli E: Distributed source coding of hyperspectral images. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS '05), July 2005, Seoul, Korea 1: 120–123.

    Google Scholar 

  8. 8.

    Tang C, Cheung N-M, Ortega A, Raghavendra CS: Efficient inter-band prediction and wavelet based compression for hyperspectral imagery: a distributed source coding approach. Proceedings of Data Compression Conference (DCC '05), March 2005, Snowbird, Utah, USA 437–446.

    Google Scholar 

  9. 9.

    Li X: Distributed coding of multispectral images: a set theoretic approach. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 2: 3105–3108.

    Google Scholar 

  10. 10.

    Puri R, Ramchandran K: PRISM: a "reversed" multimedia coding paradigm. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 1: 617–620.

    Google Scholar 

  11. 11.

    Gastpar M, Dragotti PL, Vetterli M: On compression using the distributed Karhunen-Loève transform. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 3: 901–904.

    MATH  Google Scholar 

  12. 12.

    Pradhan SS, Chou J, Ramchandran K: Duality between source coding and channel coding and its extension to the side information case. IEEE Transactions on Information Theory 2003,49(5):1181-1203. 10.1109/TIT.2003.810622

    MathSciNet  Article  Google Scholar 

  13. 13.

    Pradhan SS, Ramchandran K: Distributed source coding using syndromes (DISCUS): design and construction. IEEE Transactions on Information Theory 2003,49(3):626-643. 10.1109/TIT.2002.808103

    MathSciNet  Article  Google Scholar 

  14. 14.

    Garcia-Frias J, Zhao Y: Compression of correlated binary sources using turbo codes. IEEE Communications Letters 2001,5(10):417-419. 10.1109/4234.957380

    Article  Google Scholar 

  15. 15.

    Liveris AD, Xiong Z, Georghiades CN: Distributed compression of binary sources using conventional parallel and serial concatenated convolutional codes. Proceedings of Data Compression Conference (DCC '03), March 2003, Snowbird, Utah, USA 193–202.

    Google Scholar 

  16. 16.

    Yang Y, Cheng S, Xiong Z, Zhao W: Wyner-Ziv coding based on TCQ and LDPC codes. Proceedings of the 37th Asilomar Conference on Signals, Systems, and Computers, November 2003, Pacific Grove, Calif, USA 1: 825–829.

    Google Scholar 

  17. 17.

    Majumdar A, Chou J, Ramchandran K: Robust distributed video compression based on multilevel coset codes. Proceedings of the 37th Asilomar Conference on Signals, Systems, and Computers, November 2003, Pacific Grove, Calif, USA 1: 845–849.

    Google Scholar 

  18. 18.

    Liveris AD, Xiong Z, Georghiades CN: A distributed source coding technique for correlated images using turbo-codes. IEEE Communications Letters 2002,6(9):379-381. 10.1109/LCOMM.2002.803479

    Article  Google Scholar 

  19. 19.

    Xu Q, Xiong Z: Layered Wyner-Ziv video coding. Visual Communications and Image Processing, January 2004, San Jose, Calif, USA, Proceedings of SPIE 5308: 83–91.

    Google Scholar 

  20. 20.

    Wang H, Ortega A: Scalable predictive coding by nested quantization with layered side information. Proceedings of IEEE International Conference on Image Processing (ICIP '04), October 2004, Singapore 3: 1755–1758.

    Google Scholar 

  21. 21.

    Sehgal A, Jagmohan A, Ahuja N: Wyner-Ziv coding of video: an error-resilient compression framework. IEEE Transactions on Multimedia 2004,6(2):249-258. 10.1109/TMM.2003.822995

    Article  Google Scholar 

  22. 22.

    Said A, Pearlman WA: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 1996,6(3):243-250. 10.1109/76.499834

    Article  Google Scholar 

  23. 23.

    Grangetto M, Magli E, Olmo G: Context-based distributed wavelet video coding. Proceedings of IEEE International Workshop on Multimedia Signal Processing (MMSP '05), October-November 2005, Shanghai, China

    Google Scholar 

  24. 24.

    Taubman DS, Marcellin MW: JPEG2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic, Boston, Mass, USA; 2001.

    Google Scholar 

  25. 25.

    Penna B, Tillo T, Magli E, Olmo G: Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geoscience and Remote Sensing Letters 2006,3(1):125-129. 10.1109/LGRS.2005.859942

    Article  Google Scholar 

  26. 26.

    Gallager R: Low Density Parity Check Codes. MIT Press, Cambridge, Mass, USA; 1963.

    Google Scholar 

  27. 27.

    Liveris AD, Xiong Z, Georghiades CN: Compression of binary sources with side information at the decoder using LDPC codes. IEEE Communications Letters 2002,6(10):440-442. 10.1109/LCOMM.2002.804244

    Article  Google Scholar 

  28. 28.

    Berrou C, Glavieux A: Near optimum error correcting coding and decoding: turbo-codes. IEEE Transactions on Communications 1996,44(10):1261-1271. 10.1109/26.539767

    Article  Google Scholar 

  29. 29.

    Wu X, Memon N: Context-based, adaptive, lossless image coding. IEEE Transactions on Communications 1997,45(4):437-444. 10.1109/26.585919

    Article  Google Scholar 

  30. 30.

    MacKay DJC: Good error-correcting codes based on very sparse matrices. IEEE Transactions on Information Theory 1999,45(2):399-431. 10.1109/18.748992

    MathSciNet  Article  Google Scholar 

  31. 31.

    Chung S-Y, Forney GD Jr., Richardson TJ, Urbanke R: On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit. IEEE Communications Letters 2001,5(2):58-60. 10.1109/4234.905935

    Article  Google Scholar 

  32. 32.

    Moffat A, Neal RM, Witten IH: Arithmetic coding revisited. ACM Transactions on Information Systems 1998,16(3):256-294. 10.1145/290159.290162

    Article  Google Scholar 

  33. 33.

    Cheung N-M, Wang H, Ortega A: Correlation estimation for distributed source coding under information exchange constraints. Proceedings of IEEE International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 2: 682–685.

    Google Scholar 

  34. 34.

    Richardson TJ, Urbanke RL: Efficient encoding of low-density parity-check codes. IEEE Transactions on Information Theory 2001,47(2):638-656. 10.1109/18.910579

    MathSciNet  Article  Google Scholar 

  35. 35.

    Weinberger MJ, Seroussi G, Sapiro G: The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing 2000,9(8):1309-1324. 10.1109/83.855427

    Article  Google Scholar 

  36. 36.

    Wu X, Memon N: Context-based lossless interband compression-extending CALIC. IEEE Transactions on Image Processing 2000,9(6):994-1001. 10.1109/83.846242

    Article  Google Scholar 

  37. 37.

    Proakis JG, Salehi M: Communication Systems Engineering. 2nd edition. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.

    Google Scholar 

  38. 38.

    Cheng S, Xiong Z: Successive refinement for the Wyner-Ziv problem and layered code design. IEEE Transactions on Signal Processing 2005,53(8, part 2):3269-3281.

    MathSciNet  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Enrico Magli.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Magli, E., Barni, M., Abrardo, A. et al. Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images. EURASIP J. Adv. Signal Process. 2007, 045493 (2007). https://doi.org/10.1155/2007/45493

Download citation

Keywords

  • Image Compression
  • Hyperspectral Image
  • Real Signal
  • Compression Scheme
  • Code Technique