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Locally Optimal Partitioned Vector Quantization of Hyperspectral Data

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Hyperspectral Data Compression

VIII. Conclusions

The foregoing discussion describes in details an extension of the LBG algorithm to the locally optimal design of a partitioned vector quantizer for the encoding of source vectors drawn from a high dimensional source on ℜd. LPVQ breaks down the input space into subspaces and, for each subspace, designs a minimal-distortion vector quantizer. The partition is adaptively determined while building the quantizers in order to minimize the total distortion. Experiments on lossless and near-lossless compression of publicly available AVIRIS images show the effectiveness of the proposed method. The peculiar statistics of this class of data requires a suitable entropy coder for both quantization indices and residuals. Off-the-shelf lossless image compressors perform reasonably well on quantization indices, but it is shown how careful conditioning allows for faster and more efficient compression of the quantization indices. Applications of LPVQ to real-time compression and broadcasting, fast browsing pure-pixel classification, and real-time compression on general-purpose hardware are described in detail.

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References

  1. G. P. Abousleman. “Compression of Hyperspectral Imagery Using Hybrid DPCM/DCT and Entropy Constrained Trellis Coded Quantization,” Proceedings Data Compression Conference, IEEE Computer Society Press, 322–331, 1995.

    Google Scholar 

  2. B. Aiazzi, L. Alparone, and S. Baronti. “Near-lossless Compression of 3-D Optical Data,” IEEE Transactions on Geoscience and Remote Sensing 39:11, 2547–2557, November 2001.

    Article  Google Scholar 

  3. G. P. Abousleman, T. T. Lam, and L. J. Karam. “Robust Hyperspectral Image Coding with Channel-Optimized Trellis-Coded Quantization,” IEEE Transaction on Geoscience and Remote Sensing 40:4, 820–830, April 2002.

    Article  Google Scholar 

  4. M. J. Ryan and J. F. Arnold. “The Lossless Compression of AVIRIS Images By Vector Quantization,” IEEE Transactions on Geoscience and Remote Sensing 35:3, 546–550, May 1997.

    Article  Google Scholar 

  5. M. Manohar and J. C. Tilton. “Browse Level Compression of AVIRIS Data Using Vector Quantization on a Massively Parallel Machine,” Proceedings AVIRIS Airborne Geosciences Workshop, 2000.

    Google Scholar 

  6. M. Pickering and M. Ryan. “Efficient Spatial-Spectral Compression of Hyperspectral Data,” IEEE Transaction on Geoscience and Remote Sensing 39:7, 1536–1539, 2001.

    Article  Google Scholar 

  7. J. Mielikäinen and P. Toivanen. “Improved Vector Quantization for Lossless Compression of AVIRIS Images,” Proceedings of the XI European Signal Processing Conference, EUSIPCO-2002, EURASIP, September 2002.

    Google Scholar 

  8. S. Subramanian, N. Gat, A. Ratcliff, and M. Eismann. “Real-Time Hyperspectral Data Compression Using Principal Component Transform,” Proceedings AVIRIS Airborne Geosciences Workshop, 1992.

    Google Scholar 

  9. M. H. Sharp. “Noise-Constrained Hyperspectral Data Compression,” SPIE Optical Engineering 41:9,1–10, 2002.

    Google Scholar 

  10. J. Mielikäinen, A. Kaarna, and P. Toivanen. “Lossless Hyperspectral Image Compression via Linear Prediction,” Proceedings SPIE 4725:8, 600–608, 2002.

    Article  Google Scholar 

  11. J. Mielikäinen and P. Toivanen. “Clustered DPCM for the Lossless Compression of Hyperspectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 12, 2943–2946, December 2003.

    Article  Google Scholar 

  12. F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer. “Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction,” IEEE Signal Processing Letters, vol. 12, n. 2, February 2005.

    Google Scholar 

  13. G. Motta, F. Rizzo, and J.A. Storer. “Compression of Hyperspectral Imagery,” Proceedings Data Compression Conference, IEEE Computer Society Press, 333–342, March 2003.

    Google Scholar 

  14. F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer. “High Performance Compression of Hyperspectral Imagery with Reduced Search Complexity in the Compressed Domain,” Proceedings Data Compression Conference, IEEE Computer Society Press, 479–488, March 2004.

    Google Scholar 

  15. C. Shannon. “A Mathematical Theory of Communication,” Bell Systems Technical Journal 27, 1948. Also in “The Mathematical Theory of Communication,” C. Shannon and W. Weaver, University of Illinois Press, Urbana, IL, 1949.

    Google Scholar 

  16. S. Lloyd. “Least Squares Quantization in PCM,” IEEE Transactions on Information Theory IT-28, 1982.

    Google Scholar 

  17. J. Max. “Quantizing for Minimum Distortion,” IEEE Transactions on Information Theory IT-6:2, 1960.

    Google Scholar 

  18. J. Lin. Vector Quantization for Image Compression, Ph.D. Dissertation, Computer Science Department, Brandeis University, Waltham, MA, 1992.

    Google Scholar 

  19. Y. Linde, A. Buzo, and R. Gray. “An Algorithm for Vector Quantizer Design,” IEEE Transactions on Communications 28, 84–95, 1980.

    Article  Google Scholar 

  20. CM. Huang, Q. Bi, G.S. Stiles, and R.W. Harris. “Fast Full Search Equivalent Encoding Algorithm for Image Compression Using Vector Quantization,” IEEE Transactions on Image Processing, 413–416, July 1992.

    Google Scholar 

  21. I. Katsavounidis, C.-C.J. Kuo, and Z. Zhang. “A New Initialization Technique for Generalized Lloyd Iteration,” IEEE Signal Processing Letters, vol. 1, no. 10, 144–146, October 1994.

    Article  Google Scholar 

  22. T. Kaukoranta, P. Franti, and O. Nevalainen. “Reduced Comparison Search for the Exact GLA,” Proceedings Data Compression Conference, IEEE Computer Society Press, 33–41, March 1999.

    Google Scholar 

  23. Shen-En Qian. “Hyperspectral Data Compression Using a Fast Vector Quantization Algorithm,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, August 2004.

    Google Scholar 

  24. C. Constantinescu. Single-Pass Adaptive Vector Quantization, Ph.D. Dissertation, Computer Science Department, Brandeis University, Waltham, MA, 1995.

    Google Scholar 

  25. F. Rizzo, J. A. Storer, and B. Carpentieri. “LZ-based Image Compression,” Information Sciences, vol. 135, issues 1–2, Jun 2001.

    Google Scholar 

  26. F. Rizzo, J. A. Storer, and B. Carpentieri. “Overlap and Channel Errors in Adaptive Vector Quantization for Image Coding,” Information Sciences, vol. 171, issues 1–3, Mar 2005.

    Google Scholar 

  27. A. Gersho and R. M. Gray. Vector Quantization and Signal Compression. Kluwer Academic Press, 1991.

    Google Scholar 

  28. X. Wu. “A Tree-Structured Locally Optimal Vector Quantizer,” Proceedings Tenth International Conference on Pattern Recognition, Atlantic City, NJ, 1990.

    Google Scholar 

  29. J. Lin and J. Storer. “Improving Search for Tree-Structured Vector Quantization,” Proceedings Data Compression Conference, IEEE Computer Society Press, 339–348, 1992.

    Google Scholar 

  30. J. Lin and J. Storer. “Design and Performance of Tree-Structured Vector Quantizers,” Proceedings Data Compression Conference, IEEE Computer Society Press, 1993.

    Google Scholar 

  31. C. Barnes. Residual Vector Quantizers, Ph.D. Dissertation, Brigham Young University, 1989.

    Google Scholar 

  32. C. Barnes and R. Frost. “Necessary Conditions for the Optimality of Residual Vector Quantizers,” Proceedings IEEE International Symposium on Information Theory, 1990.

    Google Scholar 

  33. E. Riskin. Variable Rate Vector Quantization of Images, Ph.D. Dissertation, Stanford University, 1990.

    Google Scholar 

  34. F. Kossentini, M. Smith, and C. Barnes. “Image Coding with Variable Rate RVQ,” Proceedings IEEE ICASSP Conference, San Francisco, CA, 1992.

    Google Scholar 

  35. A. Viterbi and J. Omura. “Trellis Encoding of Memoryless Discrete-Time Sources with a Fidelity Criterion,” IEEE Transactions on Information Theory IT-20, 1974.

    Google Scholar 

  36. L. Colm Stewart. Trellis Data Compression, Xerox, Palo Alto Research Center, 1981.

    Google Scholar 

  37. G. Ungerboeck. “Channel Coding with Multilevel/Phase Signals,” IEEE Transactions on Information Theory IT-28, 1982.

    Google Scholar 

  38. T. Fischer, M. W. Marcellin, and M. Wang. “Trellis Coded Vector Quantization,” IEEE Transactions on Information Theory IT-37, 1991.

    Google Scholar 

  39. M. Marcellin. “Transform Coding of Images using Trellis Coded Quantization,” Proceedings International Conference on Acoustics, Speech and Signal Processing, 1990.

    Google Scholar 

  40. M. Marcellin and T. Fischer. “Trellis Coded Quantization of Memoryless and Gauss-Markov Sources,” IEEE Transactions on Communications COM-38, 1990.

    Google Scholar 

  41. G. Motta and B. Carpentieri. “A New Trellis Vector Residual Quantizer: Applications to Image Coding,” Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany, 1997.

    Google Scholar 

  42. D. Landgrebe. “Hyperspectral Image Data Analysis,” IEEE Signal Processing Magazine, 17–28, January 2002

    Google Scholar 

  43. M. G. Kendall. A course in the Geometry of n-Dimensions. New York: Hafner, 1961.

    Google Scholar 

  44. Y. Matsuyama. “Image Compression Via Vector Quantization with Variable Dimension,” TENCON 87: IEEE Region 10 Conference Computers and Communications Technology Toward 2000, Seoul, South Korea, August 1987.

    Google Scholar 

  45. JPL NASA URL: http://popo.jpl.nasa.gov/html/aviris.freedata.html.

    Google Scholar 

  46. N. M. Nasrabadi and U. Feng. “Image Compression Using Address-Vector Quantization,” IEEE Transactions on Communications, vol. 38, 2166–2173, December 1990.

    Article  Google Scholar 

  47. X. Wu, J. Wen, and W. H. Wong. “Conditional Entropy Coding of VQ Indexes for Image Compression,” Proceedings Data Compression Conference, IEEE Computer Society Press, 347–356, March 1997.

    Google Scholar 

  48. Y. Gong, M. K. H. Fan, and C. M. Huang. “Image Compression Using Lossless Coding on VQ Indexes,” Proceedings Data Compression Conference, IEEE Computer Society Press 583, March 2000.

    Google Scholar 

  49. M. J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Transactions on Image Processing, vol. 9, no. 8, 1309–1324, August 2000.

    Article  Google Scholar 

  50. NESDIS GOES R. Satellite Series Hyperspectral Sounder Data Compression Meeting, May 22, 2003.

    Google Scholar 

  51. Consulting Committee for Space Data Systems. Lossless Data Compression, Report Concerning Space Data System Standards, CCSDS 121.0-G-l, Green Book, May 1997.

    Google Scholar 

  52. Consulting Committee for Space Data Systems. Lossless Data Compression, Recommendation for Space Data Systems Standards, CCSDS 121.0-B-l, Blue Book, May 1997.

    Google Scholar 

  53. R. F. Rice, Pen-Shu Yeh, and W. H. Miller. “Algorithms for High-Speed Universal Noiseless Coding,” Proceedings AIAA Computing in Aerospace 9 Conference, San Diego, CA, October 1993.

    Google Scholar 

  54. Pen-Shu Yeh, R. F. Rice, and W. H. Miller. “On the Optimality of a Universal Noiseless Coder,” Proceedings AIAA Computing in Aerospace 9 Conference, San Diego, CA, October 1993.

    Google Scholar 

  55. Pen-Shu Yeh and W. H. Miller. “A Real Time Lossless Data Compression Technology for Remote Sensing and Other Applications,” Proceedings of ISCAS-95, 1995.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Brandeis University, Waltham, MA, 02453

    G. Motta, F. Rizzo & J. A. Storer

  2. Bitfone Corporation, Laguna Niguel, CA, 92677

    G. Motta

  3. Università degli Studi di Salerno, Baronissi SA, Italy

    F. Rizzo

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  1. G. Motta
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  2. F. Rizzo
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  3. J. A. Storer
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Editors and Affiliations

  1. Brandeis University, Massachusetts, USA

    Giovanni Motta , Francesco Rizzo  & James A. Storer ,  & 

  2. Bitfone Corporation, USA

    Giovanni Motta

  3. University of Salerno, Italy

    Francesco Rizzo

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Motta, G., Rizzo, F., Storer, J.A. (2006). Locally Optimal Partitioned Vector Quantization of Hyperspectral Data. In: Motta, G., Rizzo, F., Storer, J.A. (eds) Hyperspectral Data Compression. Springer, Boston, MA. https://doi.org/10.1007/0-387-28600-4_5

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  • DOI: https://doi.org/10.1007/0-387-28600-4_5

  • Publisher Name: Springer, Boston, MA

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  • Online ISBN: 978-0-387-28600-6

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