Backward and forward linear prediction applied to ultraspectral image processing
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Abstract
Atmospheric infrared sounder images are ultraspectral data cubes that comprise over two thousand spectral bands accounting for well over 25 megapixels of information. In this paper, we focus on the analysis of backward and forward linear prediction (LP) applied in the context of ultraspectral image compression. We start by introducing a detailed analysis of the differences and similarities between them and proceed to present a mathematical model that integrates not only error signal but also LP coefficient encoding. In addition, to overcome some of the limitations of backward LP, we present a hybrid LP scheme where both, backward and forward LP, are put into consideration by dynamically interleaving them in order to minimize the mean square error of the error signal. The model is further extended to compare all three techniques, and both experimental and theoretical samples are contrasted to verify that hybrid LP provides most efficient compression method.
Keywords
AIRS Ultraspectral Linear prediction Backward LP Forward LPReferences
- 1.Cahill, P.T., Vullo, T., Hu, J.H., Wang, Y., Deck, M.D.F., Manzo, R., Weingarten, K., Markisz, J.A.: Radiologist evaluation of a multispectral image compression algorithm for magnetic resonance images. J. Digital Imaging 3, 126–136 (1998)CrossRefGoogle Scholar
- 2.Chen, H., Wong, W., Ko, C.C.: A comparison of pitch prediction algorithms in forward and backward adaptive celp systems. In: Singapore ICCS/ISITA ’92. ‘Communications on the Move’, pp. 821–825, vol. 2 (1992). doi: 10.1109/ICCS.1992.255148
- 3.Chen, H., Wong, W., Ko, C.C.: Comparison of pitch prediction and adaptation algorithms in forward and backward adaptive celp systems. In: Communications, Speech and Vision, IEE Proceedings I , vol. 140, no. 4, pp. 240–245 (1993)Google Scholar
- 4.Chu, W.C.: Speech Coding Algorithms: Foundation and Evolution of Standardized Coders, 1st edn. Wiley, New York, NY (2003)CrossRefMATHGoogle Scholar
- 5.van Dorp, J., Kotz, S.: Generalized trapezoidal distributions. Metrika 58(1), 85–97 (2003)MathSciNetMATHGoogle Scholar
- 6.Herrero, R., Ingle, V.: Lossy compression of ultraspectral images: integrating preprocessing and compression stages. Signal Image Video Process., 1–12 (2012). doi: 10.1007/s11760-012-0397-y
- 7.Herrero, R., Ingle, V.: Space-filling curves applied to compression of ultraspectral images. Signal Image Video Process., 1–9 (2013). doi: 10.1007/s11760-013-0565-8
- 8.Herrero, R., Ingle, V.: Band ordering in compression of ultraspectral images. Signal Image Video Process. 8(2), 255–265 (2014). doi: 10.1007/s11760-013-0541-3 CrossRefGoogle Scholar
- 9.Hu, J.H., Wang, Y., Cahill, P.: Multispectral code excited linear prediction coding and its application in magnetic resonance images. IEEE Trans. Image Process. 6(11), 1555–1566 (1997). doi: 10.1109/83.641415 CrossRefGoogle Scholar
- 10.Kabir, H., Alam, S., Matin, M., Chowdhury, I.: A loss-less compression technique for high quality speech signals and its implementation with mpeg-4 als for better compression. In: 2010 IEEE International Conference on Information Theory and Information Security (ICITIS), pp. 781–785 (2010). doi: 10.1109/ICITIS.2010.5689685
- 11.Magli, E.: Multiband lossless compression of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 47(4), 1168–1178 (2009). doi: 10.1109/TGRS.2008.2009316 CrossRefGoogle Scholar
- 12.Memon, N., Sayood, K., Magliveras, S.: Lossless compression of multispectral image data. IEEE Trans. Geosci. Remote Sens. 32(2), 282–289 (1994). doi: 10.1109/36.295043 CrossRefGoogle Scholar
- 13.Nikolic, J., Peric, Z., Aleksic, D.: Optimization of \(\mu \)-law companding quantizer for laplacian source using mullers method. Przeglad Elektrotechniczny 3a, 206–208 (2013)Google Scholar
- 14.Pickering, M., Ryan, M.: An architecture for the compression of hyperspectral imagery. In: Hyperspectral Data Compression, pp. 1–34 (2006)Google Scholar
- 15.Shuhong, W., Gang, Z.: 8 kbit/s ld-acelp speech coding with backward pitch detection. In: Asia-Pacific Conference on Information Processing, vol. 2, pp. 434–437 (2009). doi: 10.1109/APCIP.2009.243
- 16.Zhang, J., Liu, G.: An efficient reordering prediction-based lossless compression algorithm for hyperspectral images. IEEE Geosci. Remote Sens. Lett. 4(2), 283–287 (2007). doi: 10.1109/LGRS.2007.890546 CrossRefGoogle Scholar