Abstract
To meet quality issues of hyperspectral imaging, differential pulse code modulation (DPCM) is usually employed for either lossless or near-lossless data compression, i.e., the decompressed data have a user-defined maximum absolute error, being zero in the lossless case. Lossless compression thoroughly preserves the information of the data but allows a moderate decrement in transmission bit rate. Lossless compression ratios attained even by the most advanced schemes are not very high and usually lower than four. If strictly lossless techniques are not employed, a certain amount of information of the data will be lost. However, such an information may be partly due to random fluctuations of the instrumental noise. The rationale that compression-induced distortion is more tolerable, i.e., less harmful, in those bands, in which the noise is higher, and vice-versa, constitutes the virtually lossless paradigm.
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References
Abrardo, A., Alparone, L., Bartolini, F.: Encoding-interleaved hierarchical interpolation for lossless image compression. Signal Processing 56(2), 321–328 (1997)
Aiazzi, B., Alba, P., Alparone, L., Baronti, S.: Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction. IEEE Trans. Geosci. Remote Sensing 37(5), 2287–2294 (1999)
Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., Marcoionni, P., Pippi, I., Selva, M.: Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers. Annals of Geophysics 41(1), 1–9 (2006)
Aiazzi, B., Alparone, L., Barducci, A., Baronti, S., Pippi, I.: Estimating noise and information of multispectral imagery. J. Optical Engin. 41(3), 656–668 (2002)
Aiazzi, B., Alparone, L., Baronti, S.: A reduced Laplacian pyramid for lossless and progressive image communication. IEEE Trans. Commun. 44(1), 18–22 (1996)
Aiazzi, B., Alparone, L., Baronti, S.: Near-lossless compression of 3-D optical data. IEEE Trans. Geosci. Remote Sensing 39(11), 2547–2557 (2001)
Aiazzi, B., Alparone, L., Baronti, S.: Context modeling for near-lossless image coding. IEEE Signal Processing Lett. 9(3), 77–80 (2002)
Aiazzi, B., Alparone, L., Baronti, S.: Fuzzy logic-based matching pursuits for lossless predictive coding of still images. IEEE Trans. Fuzzy Systems 10(4), 473–483 (2002)
Aiazzi, B., Alparone, L., Baronti, S.: Near-lossless image compression by relaxation-labelled prediction. Signal Processing 82(11), 1619–1631 (2002)
Aiazzi, B., Alparone, L., Baronti, S.: Lossless compression of hyperspectral images using multiband lookup tables. IEEE Signal Processing Lett. 16(6), 481–484 (2009)
Aiazzi, B., Alparone, L., Baronti, S., Lastri, C.: Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 4(4), 532–536 (2007)
Aiazzi, B., Alparone, L., Baronti, S., Lotti, F.: Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid. IEEE Trans. Image Processing 6(6), 831–843 (1997)
Aiazzi, B., Alparone, L., Baronti, S., Santurri, L.: Near-lossless compression of multi/hyperspectral images based on a fuzzy-matching-pursuits interband prediction. In: S.B. Serpico (ed.) Image and Signal Processing for Remote Sensing VII, vol. 4541, pp. 252–263 (2002)
Alecu, A., Munteanu, A., Cornelis, J., Dewitte, S., Schelkens, P.: On the optimality of embedded deadzone scalar-quantizers for wavelet-based L-infinite-constrained image coding. IEEE Signal Processing Lett. 11(3), 367–370 (2004)
Alecu, A., Munteanu, A., Cornelis, J., Dewitte, S., Schelkens, P.: Wavelet-based scalable L-infinity-oriented compression. IEEE Trans Image Processing 15(9), 2499–2512 (2006)
Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition–Parts I and II. IEEE Trans. Syst. Man Cybern.–B 29(6), 778–800 (1999)
Benazza-Benyahia, A., Pesquet, J.C., Hamdi, M.: Vector-lifting schemes for lossless coding and progressive archival of multispectral images. IEEE Trans. Geosci. Remote Sensing 40(9), 2011–2024 (2002)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum Press, New York (1981)
Carpentieri, B., Weinberger, M.J., Seroussi, G.: Lossless compression of continuous-tone images. Proc. of the IEEE 88(11), 1797–1809 (2000)
Chang, C.I.: An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Trans. Inform. Theory 46(5), 1927–1932 (2000)
Deng, G., Ye, H., Cahill, L.W.: Adaptive combination of linear predictors for lossless image compression. IEE Proc.-Sci. Meas. Technol. 147(6), 414–419 (2000)
Golchin, F., Paliwal, K.K.: Classified adaptive prediction and entropy coding for lossless coding of images. In: Proc. IEEE Int. Conf. on Image Processing, vol. III/III, pp. 110–113 (1997)
Huang, B., Sriraja, Y.: Lossless compression of hyperspectral imagery via lookup tables with predictor selection. In: L. Bruzzone (ed.) Proc. of SPIE, Image and Signal Processing for Remote Sensing XII, vol. 6365, pp. 63650L.1–63650L.8 (2006)
Jayant, N.S., Noll, P.: Digital Coding of Waveforms: Principles and Applications to Speech and Video. Prentice Hall, Englewood Cliffs, NJ (1984)
Ke, L., Marcellin, M.W.: Near-lossless image compression: minimum entropy, constrained-error DPCM. IEEE Trans. Image Processing 7(2), 225–228 (1998)
Keshava, N.: Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries. IEEE Trans. Geosci. Remote Sensing 42(7), 1552–1565 (2004)
Kiely, A.B., Klimesh, M.A.: Exploiting calibration-induced artifacts in lossless compression of hyperspectral imagery. IEEE Trans. Geosci. Remote Sensing 47(8), 2672–2678 (2009)
Klimesh, M.: Low-complexity adaptive lossless compression of hyperspectral imagery. In: Satellite Data Compression, Communication and Archiving II, Proc. SPIE, vol. 6300 pp. 63000N.1–63000N.9 (2006)
Lastri, C., Aiazzi, B., Alparone, L., Baronti, S.: Virtually lossless compression of astrophysical images. EURASIP Journal on Applied Signal Processing 2005(15), 2521–2535 (2005)
Magli, E., Olmo, G., Quacchio, E.: Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC. IEEE Geosci. Remote Sensing Lett. 1(1), 21–25 (2004)
Matsuda, I., Mori, H., Itoh, S.: Lossless coding of still images using minimum-rate predictors. In: Proc. IEEE Int. Conf. on Image Processing, vol. I/III, pp. 132–135 (2000)
Mielikainen, J.: Lossless compression of hyperspectral images using lookup tables. IEEE Signal Proc. Lett. 13(3), 157–160 (2006)
Mielikainen, J., Toivanen, P.: Clustered DPCM for the lossless compression of hyperspectral images. IEEE Trans. Geosci. Remote Sensing 41(12), 2943–2946 (2003)
Mielikainen, J., Toivanen, P., Kaarna, A.: Linear prediction in lossless compression of hyperspectral images. J. Optical Engin. 42(4), 1013–1017 (2003)
Penna, B., Tillo, T., Magli, E., Olmo, G.: Progressive 3-D coding of hyperspectral images based on JPEG 2000. IEEE Geosci. Remote Sensing Lett. 3(1), 125–129 (2006)
Pennebaker, W.B., Mitchell, J.L.: JPEG: Still Image Compression Standard. Van Nostrand Reinhold, New York (1993)
Ramabadran, T.V., Chen, K.: The use of contextual information in the reversible compression of medical images. IEEE Trans. Medical Imaging 11(2), 185–195 (1992)
Rao, A.K., Bhargava, S.: Multispectral data compression using bidirectional interband prediction. IEEE Trans. Geosci. Remote Sensing 34(2), 385–397 (1996)
Rao, K.K., Hwang, J.J.: Techniques and Standards for Image, Video, and Audio Coding. Prentice Hall, Engl. Cliffs, NJ (1996)
Reichel, J., Menegaz, G., Nadenau, M.J., Kunt, M.: Integer wavelet transform for embedded lossy to lossless image compression. IEEE Trans. Image Processing 10(3), 383–392 (2001)
Rice, R.F., Plaunt, J.R.: Adaptive variable-length coding for efficient compression of spacecraft television data. IEEE Trans. Commun. Technol. COM-19(6), 889–897 (1971)
Rizzo, F., Carpentieri, B., Motta, G., Storer, J.A.: Low-complexity lossless compression of hyperspectral imagery via linear prediction. IEEE Signal Processing Lett. 12(2), 138–141 (2005)
Roger, R.E., Cavenor, M.C.: Lossless compression of AVIRIS images. IEEE Trans. Image Processing 5(5), 713–719 (1996)
Said, A., Pearlman, W.A.: An image multiresolution representation for lossless and lossy compression. IEEE Trans. Image Processing 5(9), 1303–1310 (1996)
Tate, S.R.: Band ordering in lossless compression of multispectral images. IEEE Trans. Comput. 46(4), 477–483 (1997)
Taubman, D.S., Marcellin, M.W.: JPEG2000: Image compression fundamentals, standards and practice. Kluwer Academic Publishers, Dordrecht, The Netherlands (2001)
Wang, J., Zhang, K., Tang, S.: Spectral and spatial decorrelation of Landsat-TM data for lossless compression. IEEE Trans. Geosci. Remote Sensing 33(5), 1277–1285 (1995)
Weinberger, M.J., Rissanen, J.J., Arps, R.B.: Applications of universal context modeling to lossless compression of gray-scale images. IEEE Trans. Image Processing 5(4), 575–586 (1996)
Weinberger, M.J., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Trans. Image Processing 9(8), 1309–1324 (2000)
Witten, I.H., Neal, R.M., Cleary, J.G.: Arithmetic coding for data compression. Commun. ACM 30, 520–540 (1987)
Wu, X., Bao, P.: L ∞ constrained high-fidelity image compression via adaptive context modeling. IEEE Trans. Image Processing 9(4), 536–542 (2000)
Wu, X., Memon, N.: Context-based, adaptive, lossless image coding. IEEE Trans. Commun. 45(4), 437–444 (1997)
Wu, X., Memon, N.: Context-based lossless interband compression–Extending CALIC. IEEE Trans. Image Processing 9(6), 994–1001 (2000)
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Aiazzi, B., Alparone, L., Baronti, S. (2012). Quality Issues for Compression of Hyperspectral Imagery Through Spectrally Adaptive DPCM. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_6
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