Abstract
Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Byrne, J., Ierodiaconou, S., Bull, D., Redmill, D., Hill, P.: Unsupervised image compression-by-synthesis within a jpeg framework. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 2892–2895. IEEE (2008)
Ciżnicki, M., Kierzynka, M., Kopta, P., Kurowski, K., Gepner, P.: Benchmarking jpeg 2000 implementations on modern cpu and gpu architectures. Journal of Computational Science 5(2), 90–98 (2014)
Du, Q., Fowler, J.E.: Hyperspectral image compression using jpeg2000 and principal component analysis. IEEE Geoscience and Remote Sensing Letters 4(2), 201–205 (2007)
Du, Q., Ly, N., Fowler, J.E.: An operational approach for hyperspectral image compression. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1357–1360 (2012)
Gao, X., Lu, W., Tao, D., Li, X.: Image quality assessment based on multiscale geometric analysis. IEEE Transactions on Image Processing 18(7), 1409–1423 (2009)
Huo, C., Zhang, R., Peng, T.: Lossless compression of hyperspectral images based on searching optimal multibands for prediction. IEEE Geoscience and Remote Sensing Letters 6(2), 339–343 (2009)
Jun, Z.: Hyperspectral imaging in environmental informatics. In: Seminar Presentation at Charles Sturt University (April 16, 2014)
Karami, A., Yazdi, M., Mercier, G.: Hyperspectral image compression based on tucker decomposition and wavelet transform. In: 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4. IEEE (2011)
Liu, A., Lin, W., Zhang, F.: Lossless video compression with optimal compression plane determination. In: IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 173–176. IEEE (2009)
Liu, A., Lin, W., Paul, M., Zhang, F., Deng, C.: Optimal compression plane for efficient video coding. IEEE Transactions on Image Processing 20(10), 2788–2799 (2011)
Liu, G., Zhao, F., Qu, G.: An efficient compression algorithm for hyperspectral images based on a modified coding framework of h. 264/avc. In: IEEE International Conference on Image Processing, San Antonio, TX, USA, pp. 341–344 (2007)
Noor, N.R.M., Vladimirova, T.: Investigation into lossless hyperspectral image compression for satellite remote sensing. International Journal of Remote Sensing 34(14), 5072–5104 (2013)
Miaou, S.-G., Ke, F.-S., Chen, S.-C.: A lossless compression method for medical image sequences using jpeg-ls and interframe coding. IEEE Transactions on Information Technology in Biomedicine 13(5), 818–821 (2009)
Motta, G., Rizzo, F., Storer, J.A.: Hyperspectral data compression. Springer (2006)
Mudassar Raza, A.A., Sharif, M., Haider, S.W.: Lossless compression method for medical image sequences using super-spatial structure prediction and inter-frame coding. J. Appl. Res. Technol. 10(4), 618–628 (2012)
Paul, M.: Efficient video coding using optimal compression plane and background modelling. IET Image Processing 6(9), 1311–1318 (2012)
Rucker, J.T., Fowler, J.E., Younan, N.H.: Jpeg2000 coding strategies for hyperspectral data. In: Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, vol. 1, p. 4. IEEE (2005)
Santos, L., López, S., Callico, G.M., Lopez, J.F., Sarmiento, R.: Performance evaluation of the h. 264/avc video coding standard for lossy hyperspectral image compression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(2), 451–461 (2012)
Sullivan, G.J., Ohm, J., Han, W.J., Wiegand, T.: Overview of the high efficiency video coding (hevc) standard. IEEE Transactions on Circuits and Systems for Video Technology 22(12), 1649–1668 (2012)
Tang, X., Pearlman, W.A., Modestino, J.W.: Hyperspectral image compression using three-dimensional wavelet coding. In: Electronic Imaging 2003, pp. 1037–1047. International Society for Optics and Photonics (2003)
Vo, D.T., Nguyen, T.Q.: Quality enhancement for motion jpeg using temporal redundancies. IEEE Transactions on Circuits and Systems for Video Technology 18(5), 609–619 (2008)
Wang, Z., Chanda, D., Simon, S., Richter, T.: Memory efficient lossless compression of image sequences with jpeg-ls and temporal prediction. In: Picture Coding Symposium (PCS), pp. 305–308. IEEE (2012)
Wu, Y.Q., Wu, C.: Hyperspectral remote sensing image compression based on wavelet and support vector regression. Journal of Astronautics 3, 024 (2011)
Zhu, W., Du, Q., Fowler, J.E.: Multitemporal hyperspectral image compression. IEEE Geoscience and Remote Sensing Letters 8(3), 416–420 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xiao, R., Paul, M. (2015). Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8935. Springer, Cham. https://doi.org/10.1007/978-3-319-14445-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-14445-0_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14444-3
Online ISBN: 978-3-319-14445-0
eBook Packages: Computer ScienceComputer Science (R0)