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Adaptive Encoding Algorithm for Multispectral Images

by S.Deepa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 1
Year of Publication: 2010
Authors: S.Deepa
10.5120/19-126

S.Deepa . Adaptive Encoding Algorithm for Multispectral Images. International Journal of Computer Applications. 1, 1 ( February 2010), 60-66. DOI=10.5120/19-126

@article{ 10.5120/19-126,
author = { S.Deepa },
title = { Adaptive Encoding Algorithm for Multispectral Images },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 1 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 60-66 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number1/19-126/ },
doi = { 10.5120/19-126 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:40.778045+05:30
%A S.Deepa
%T Adaptive Encoding Algorithm for Multispectral Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 1
%P 60-66
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A new adaptive multispectral image compression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class of the region, appropriate encoding technique is chosen. The image is first segmented by means of Region splitting and merging procedure based on the statistical characteristic of the image. Then class adaptive hotelling transform or Karhunen Loeve transform (KLT) in the spectral domain and the shape adaptive wavelet transform in the spatial domain are adopted in the image by considering the spatial, spectral and statistical properties which are unique to the multispectral images. The quadtree is used for determining the transform block size and a single KLT matrix is used for the regions of same class ie., class adaptive KLT is applied and the transformation is followed by shape adaptive wavelet transform(SAWT) incorporating the spatial and structural properties of the multispectral image. After transformation, based on the regions identified, if the region is relatively uniform or smooth, the SPIHT (Set Partitioning in Hierarchical Trees) algorithm is adopted. If not, that is, if the region is highly textured in nature, then object based wavelet method is used for compression. Thus the advantages of both SPIHT algorithm and object based wavelet encoding method, both in terms of visual quality and PSNR values, are incorporated in a single compression technique.

References
  1. A. Gersho and R. M. Gray, Vector Quantization and Signal Compression. Boston, MA: Kluwer, 1992.
  2. A. Said and W. A. Pearlman, "A new fast and efficient image codec based on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst. Video Technol., vol. 6, no. 6, pp. 243-250, Jun. 1996.
  3. B. Kim and W. A. Pearlman, "An embedded wavelet video coder using three-dimensional set partitioning in hierarchical trees," in Proc. Data Compression Conf., Mar. 1997, pp. 251-260.
  4. B. Penna, T. Tillo, E. Magli, and G. Olmo, "A new low complexity KLT for lossy hyperspectral data compression," in Proc. IEEE Int. Geoscience and Remote Sensing Symp., Aug. 2006, pp. 3528-3528.
  5. B. Penna, T. Tillo, E. Magli, and G. Olmo, "Transform coding techniques for lossy hyperspectral data compression," IEEE Geosci. Remote Sens. Lett., vol. 45, no. 5, pp. 1408-1421, May 2007.
  6. C. D'Elia, G. Poggi, and G. Scarpa, "A tree-structured markov random field model for bayesian image segmentation," IEEE Trans. Image Process., vol. 12, no. 10, pp. 1259-1273, Oct. 2003.
  7. G. Fernandez and C. M. Wittenbrink, "Coding of spectrally homogeneous regions in multispectral image compression," in Proc. IEEE Int. Conf. Image Processing, Lausanne, Switzerland, Sep. 1996, vol. 2, pp. 923-926.
  8. G. Gelli and G. Poggi, "Compression of multispectral images by spectral classification and transform coding," IEEE Trans. Image Process., vol. 8, no. 4, pp. 476-489, Apr. 1999.
  9. G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, "Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT," IEEE Trans. Geosci. Remote Sens., vol. 33, no. 1, pp. 26-34, Jan. 1995.
  10. G. Poggi and A. R. P. Ragozini, "Image segmentation by tree-structured Markov random fields," IEEE Signal Process. Lett., vol. 6, no. 7, pp. 155-157, Jul. 1999.
  11. G. R. Canta and G. Poggi, "Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding," IEEE Trans. Image Process., vol. 7, no. 5, pp. 668-678, May 1998.
  12. J. A. Saghri, A. G. Tescher, and J. T. Reagan, "Practical transform coding of multispectral imagery," IEEE Signal Process. Mag., vol. 12, no. 1, pp. 32-43, Jan. 1995.
  13. J. E. Fowler and D. N. Fox, "Embedded wavelet-based coding of threedimensional oceanographic images with land masses," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 1, pp. 284-290, Feb. 2001.
  14. J. Lee, "Optimized quadtree for Karhunen-Loève transform in multispectral image coding," IEEE Trans. Image Process., vol. 8, no. 4, pp.453-461, Apr. 1999.
  15. Marco Cagnazzo, Giovanni Poggi, and Luisa Verdoliva, "Region-Based Transform Coding of Multispectral Images", IEEE Transactions On Image Processing, Vol. 16, No. 12, Pg. 2916-2927, December 2007
  16. M. Cagnazzo, S. Parrilli, G. Poggi, and L. Verdoliva, "Improved class-based coding of multispectral images with shape-adaptive wavelet transform," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, pp. 566-570, Oct. 2007.
  17. M. Cagnazzo, S. Parrilli, G. Poggi, and L. Verdoliva, "Costs and advantages of object-based image coding with shape-adaptive wavelet transform," Int. J. Image Video Process., vol. 2007, p. 13, 2007, Article ID 78323.
  18. M. Cagnazzo, G. Poggi, L. Verdoliva, and A. Zinicola, "Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT," in Proc. IEEE Int. Conf. Image Processing, Singapore, Oct. 2004, pp. 2459-2462.
  19. M. Finelli, G. Gelli, and G. Poggi, "Multispectral image coding by spectral classification," in Proc. IEEE Int. Conf. Image Process., Lausanne, Switzerland, Sep. 1996, vol. 2, pp. 605-608.
  20. M. Petrou, P. Hou, S. Kamata, and C. I. Underwood, "Region-based image coding with multiple algorithms," IEEE Trans. Geosci. Remote Sens., vol. 41, no. 3, pp. 562-570, Mar. 2001.
  21. Nikola Sprljan, Sonja Grgic, Mislav Grgic, "Modified SPIHT algorithm for wavelet packet image coding" Real-Time Imaging 11 (2005) 378-388 , www.sciencedirect.com
  22. P. L. Dragotti, G. Poggi, and A. R. P. Ragozini, "Compression of multispectral images by three-dimensional SPIHT algorithm," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 416-428, Jan. 2000.
  23. Q. Du and J. E. Fowler, "Hyperspectral image compression using JPEG2000 and principal component analysis," IEEE Geosci. Remote Sens. Lett., vol. 45, no. 4, pp. 201-205, Apr. 2007.
  24. S. E. Qian, "Hyperspectral data compression using a fast vector quantization algorithm," IEEE Trans. Geosci. Remote Sens., vol. 42, no. pp. 1791-1798, Aug. 2004.
  25. S. Gupta and A. Gersho, "Feature predictive vector quantization multispectral images," IEEE Geosci. Remote Sens. Lett., vol. 30, no. 5, pp. 491-501, May 1992.
  26. S. Li and W. Li, "Shape-adaptive discrete wavelet transforms for arbitrarily shaped visual object coding," IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 8, pp. 725-743, Aug. 2000.
Index Terms

Computer Science
Information Sciences

Keywords

class adaptive KL transform Quadtree shape adaptive WT Adaptive encoding technique

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