Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 825673, 7 pages
http://dx.doi.org/10.1155/2013/825673
Research Article

Low-Complexity Compression Algorithm for Hyperspectral Images Based on Distributed Source Coding

1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China
2School of Biomedical Engineering, Third Military Medical University and Chongqing University, Chongqing 400038, China

Received 17 July 2013; Revised 20 August 2013; Accepted 20 August 2013

Academic Editor: Gelan Yang

Copyright © 2013 Yongjian Nian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. I. Blanes and J. Serra-Sagrista, “Pairwise orthogonal transform for spectral image coding,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 961–972, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1408–1421, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Tang, S. Cho, and W. A. Pearlman, “3D set partitioning coding methods in hyperspectral image compression,” in Proceedings of the International Conference on Image Processing (ICIP '03), pp. 239–242, Barcelona, Spain, September 2003. View at Scopus
  4. E. Magli, “Multiband lossless compression of hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 4, pp. 1168–1178, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Tang, Q. Xin, G. Li, and J.-W. Wan, “Lossless compression of hyperspectral images based on contents,” Optics and Precision Engineering, vol. 20, no. 3, pp. 668–674, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. S. S. Pradhan and K. Ramchandran, “Distributed source coding using syndromes (DISCUS): design and construction,” IEEE Transactions on Information Theory, vol. 49, no. 3, pp. 626–643, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  7. Z. Xiong, A. D. Liveris, and S. Cheng, “Distributed source coding for sensor networks,” IEEE Signal Processing Magazine, vol. 21, no. 5, pp. 80–94, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. D. Slepian and J. K. Wolf, “Noiseless coding of correlated information sources,” IEEE Transactions on Information Theory, vol. 19, no. 4, pp. 471–480, 1973. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  9. A. D. Wyner and J. Ziv, “The rate-distortion function for source coding with side information at the decoder,” IEEE Transactions on Information Theory, vol. 22, no. 1, pp. 1–10, 1976. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  10. X. Pan, R. Liu, and X. Lv, “Low-complexity compression method for hyperspectral images based on distributed source coding,” IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 2, pp. 224–227, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. C. Tang, N.-M. Cheung, A. Ortega, and C. S. Raghavendra, “Efficient inter-band prediction and wavelet based compression for hyperspectral imagery: a distributed source coding approach,” in Proceedings of the Data Compression Conference, pp. 437–446, Snowbird, Utah, March 2005. View at Scopus
  12. N.-M. Cheung, H. Wang, and A. Ortega, “Sampling-based correlation estimation for distributed source coding under rate and complexity constraints,” IEEE Transactions on Image Processing, vol. 17, no. 11, pp. 2122–2137, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  13. E. Magli, M. Barni, A. Abrardo, and M. Grangetto, “Distributed source coding techniques for lossless compression of hyperspectral images,” EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 45493, 13 pages, 2007. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. A. Abrardo, M. Barni, E. Magli, and F. Nencini, “Error-resilient and low-complexity onboard lossless compression of hyperspectral images by means of distributed source coding,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 4, pp. 1892–1904, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Abrardo, M. Barni, and E. Magli, “Low-complexity lossy compression of hyperspectral images via informed quantization,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 505–508, Hong Kong, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. J. Nian, Q. Xin, Y. Tang, and J. W. Wan, “Distributed lossless compression of hyperspectral images based on multi-band prediction,” Optics and Precision Engineering, vol. 20, no. 4, pp. 906–912, 2012. View at Google Scholar
  17. Y. J. Nian, J. W. Wan, Y. Tang, and B. Chen, “Near lossless compression of hyperspectral images based on distributed source coding,” Science China Information Sciences, vol. 55, no. 11, pp. 2646–2655, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  18. A. Abrardo, M. Barni, and E. Magli, “Low-complexity predictive lossy compression of hyperspectral and ultraspectral images,” in Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '11), pp. 797–800, Prague, Czech Republic, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. X. Wu and N. Memon, “Context-based lossless interband compression: extending CALIC,” IEEE Transactions on Image Processing, vol. 9, no. 6, pp. 994–1001, 2000. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Mielikainen, “Lossless compression of hyperspectral images using lookup tables,” IEEE Signal Processing Letters, vol. 13, no. 3, pp. 157–160, 2006. View at Publisher · View at Google Scholar · View at Scopus