Abstract
This chapter gives an overview of the lookup table (LUT) based lossless compression methods for hyperspectral images. The LUT method searches the previous band for a pixel of equal value to the pixel co-located to the one to be coded. The pixel in the same position as the obtained pixel in the current band is used as the predictor. Lookup tables are used to speed up the search. Variants of the LUT method include predictor guided LUT method and multiband lookup tables.
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References
A. Bilgin, G. Zweig, and M. Marcellin, “Three-dimensional image compression with integer wavelet transforms,” Appl. Opt., vol. 39, no. 11, pp. 1799–1814, Apr. 2000.
B. Baizert, M. Pickering, and M. Ryan, “Compression of hyperspectral data by spatial/spectral discrete cosine transform,” in Proc. Int. Geosci. Remote Sens. Symp., 2001, vol. 4, pp. 1859–1861, doi: 10.1109/IGARSS.2001.977096.
J. Mielikainen and A. Kaarna, “Improved back end for integer PCA and wavelet transforms for lossless compression of multispectral images,” in Proc. 16th Int. Conf. Pattern Recog., Quebec City, QC, Canada, 2002, pp. 257–260, doi: 10.1109/ICPR.2002.1048287.
M. Ryan and J. Arnold, “The lossless compression of AVIRIS images vector quantization,” IEEE Trans. Geosci. Remote Sens., vol. 35, no. 3, pp. 546–550, May 1997, doi: 10.1109/36.581964.
J. Mielikainen and P. Toivanen, “Improved vector quantization for lossless compression of AVIRIS images,” in Proc. XI Eur. Signal Process. Conf., Toulouse, France, Sep. 2002, pp. 495–497.
G. Motta, F. Rizzo, and J. Storer, “Partitioned vector quantization application to lossless compression of hyperspectral images,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Jul. 2003, vol. 1, pp. 553–556, doi: 10.1109/ICME.2003.1220977.
S. Tate, “Band ordering in lossless compression of multispectral images,” IEEE Trans. Comput., vol. 46, no. 4, pp. 477–483, Apr. 1997, doi: 10.1109/12.588062.
P. Toivanen, O. Kubasova, and J. Mielikainen, “Correlation-based bandordering heuristic for lossless compression of hyperspectral sounder data,” IEEE Geosci. Remote Sens. Lett., vol. 2, no. 1, pp. 50–54, Jan. 2005, doi: 10.1109/LGRS.2004.838410.
J. Zhang and G. Liu, “An efficient reordering prediction based lossless compression algorithm for hyperspectral images,” IEEE Geosci. Remote Sens. Lett., vol. 4, no. 2, pp. 283–287, Apr. 2007, doi: 10.1109/LGRS.2007.890546.
A. Abrardo, M. Barni, E. Magli, F. Nencini, “Error-Resilient and Low-Complexity On-board Lossless Compression of Hyperspectral Images by Means of Distributed Source Coding,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 4, pp. 1892–1904, 2010, doi:10.1109/TGRS.2009.2033470.
A. B. Kiely, M. A. Klimesh, “Exploiting Calibration-Induced Artifacts in Lossless Compression of Hyperspectral Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2672–2678, 2009, doi:10.1109/TGRS.2009.2015291.
M. Slyz, L. Zhang, “A block-based inter-band lossless hyperspectral image compressor,” in Proc. of IEEE Data Compression Conference, pp. 427–436, 2005, doi: 10.1109/DCC.2005.1.
C.-C. Lin, Y.-T. Hwang., “An Efficient Lossless Compression Scheme for Hyperspectral Images Using Two-Stage Prediction”, vol. 7, no. 3, pp. 558–562, 2010, doi:10.1109/LGRS.2010.2041630.
J. Mielikainen, P. Toivanen, “Clustered DPCM for the Lossless Compression of Hyperspectral Images”, IEEE Trans. Geosci. Remote Sens., vol. 41, no. 12, pp. 2943–2946, 2003 doi:10.1109/TGRS.2003.820885.
B. Aiazzi, L. Alparone, S. Baronti, and C. Lastri, “Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery,” IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, pp. 532–536, Oct. 2007, 10.1109/LGRS.2007.900695.
E. Magli, G. Olmo, and E. Quacchio, “Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC,” IEEE Geosci. Remote Sens. Lett., vol. 1, no. 1, pp. 21–25, Jan. 2004, doi:10.1109/LGRS.2003.822312.
F. Rizzo, B. Carpentieri, G. Motta, and J. Storer, “Low-complexity lossless compression o hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett., vol. 12, no. 2, pp. 138–141, Feb. 2005, doi:10.1109/LSP.2004.840907.
J. Mielikainen and P. Toivanen, “Parallel implementation of linear prediction model for lossless compression of hyperspectral airborne visible infrared imaging spectrometer images,” J. Electron. Imaging, vol. 14, no. 1, pp. 013010-1–013010-7, Jan.–Mar. 2005, doi:10.1117/1.1867998.
H. Wang, S. Babacan, and K. Sayood, “Lossless Hyperspectral-Image Compression Using Context-Based Conditional Average,” IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 4187–8193, Dec. 2007, doi:0.1109/TGRS.2007.906085.
M. Slyz and L. Zhang, “A block-based inter-band lossless hyperspectral image compressor,” in Proc. Data Compression Conf., Snowbird, UT, 2005, pp. 427–436, doi:10.1109/DCC.2005.1.
S. Jain and D. Adjeroh, “Edge-based prediction for lossless compression of hyperspectral images,” in Proc. Data Compression Conf., Snowbird, UT, 2007, pp. 153–162, doi:10.1109/DCC.2007.36.
J. Mielikainen, “Lossless compression of hyperspectral images using lookup tables,” IEEE Sig. Proc. Lett., vol. 13, no. 3, pp. 157–160, 2006, doi:10.1109/LSP.2005.862604.
E. Magli, “Multiband lossless compression of hyperspectral images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 4, pp. 1168–1178, Apr. 2009, doi:10.1109/TGRS.2008.2009316.
J. Mielikainen, P. Toivanen, “Lossless Compression of Hyperspectral Images Using a Quantized Index to Lookup Tables,” vol. 5, no. 3, pp. 474–477, doi:10.1109/LGRS.2008.917598.
J. Mielikainen, P. Toivanen, and A. Kaarna, “Linear prediction in lossless compression of hyperspectral images,” Opt. Eng., vol. 42, no. 4, pp. 1013–1017, Apr. 2003, doi:10.1117/1.1557174.
B. Aiazzi, S. Baronti, S., L. Alparone, “Lossless Compression of Hyperspectral Images Using Multiband Lookup Tables,” IEEE Signal Processing Letters, vol. 16, no. 6, pp. 481–484. Jun. 2009, doi:10.1109/LSP.2009.2016834, 0.1109/LSP.2009.2016834.
W. Porter and H. Enmark, “A system overview of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Proc. SPIE, vol. 834, pp. 22–31, 1997
X. Wu and N. Memon, “Context-based lossless interband compression—Extending CALIC,” IEEE Trans. Image Process., vol. 9, no. 6, pp. 994–1001, Jun. 2000, doi:10.1109/83.846242.
B. Huang, Y. Sriraja, “Lossless compression of hyperspectral imagery via lookup tables with predictor selection,” in Proc. SPIE, vol. 6365, pp. 63650L.1–63650L.8, 2006, doi:10.1117/12.690659.
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This work was supported by the Academy of Finland.
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Mielikainen, J. (2012). Lookup-Table Based Hyperspectral Data Compression. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_8
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