한국지구과학회지 (Journal of the Korean earth science society)

  • 제32권6호
  • /
  • Pages.575-585
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  • 2011
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  • 1225-6692(pISSN)
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  • 2287-4518(eISSN)
한국지구과학회 (The Korean Earth Science Society)
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인공위성 영상 압축에 있어 웨이브렛 선택의 효과

The Effect of Wavelet Pair Choice in the Compression of the Satellite Images

  • Jin, Hong-Sung (Department of Applied Mathematics, Chonnam National University) ;
  • Han, Dong-Yeob (Department of Marine and Civil Engineering, Chonnam National University)
  • 투고 : 2011.07.04
  • 심사 : 2011.08.12
  • 발행 : 2011.10.31
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초록

웨이브렛 변환을 이용한 인공위성 영상의 압축을 보다 효과적으로 수행할 수 있는 필터의 조합을 살펴보았다. 압축정도를 표현하는 방법으로 encoding ratio를 이용하였고, 웨이브렛의 선택에 따라 PSNR값과 encoding ratio값이 변하는 양상을 보았다. 웨이브렛의 선택에 따라 PSNR값은 대략 13.2-21.6% 정도의 차이를 보였고 encoding ratio는 16.8-45.5%까지의 차이를 보였다. SAR 영상의 경우 encoding ratio는 16%20% 변동이 있지만, 일반영상의 경우는 웨이브렛의 선택에 따라 40% 이상 변화였다. 영상 압축시 웨이브렛의 선택효과는 인공위성 영상보다 일반영상에서 큰 영향을 미쳤다. PSNR, encoding ratio의 지수에서 인공위성 영상은 웨이브렛 선택에 영향을 덜 받는다. 인공위성 영상의 압축에 대한 웨이브렛 선택효과를 보여주기 위하여 새로운 지수인 ECR을 제안하였다. ECR은 일반영상보다 인공위성영상에서 웨이브렛의 종류에 따른 영향이 더 적게 나타났다. 그러나 인공위성 영상 압축시 3개의 지수에서 웨이브렛의 선택효과는 최소한 10% 이상으로 선택의 중요성은 무시될 수 없을 것이다.

The effect of wavelet pair choice in the compression of the satellite images is studied. There is a trade-off between compression rate and perception quality. The encoding ratio is used to express the compression rate, and Peak Signal-to-Noise Ratio (PSNR) is also used for the perceptional performance. The PSNR and the encoding ratio are not matched well for the images with various wavelet pairs, but the tendency is remarkable. It is hard to find the pattern of PSNR for sampled images. On the other hand, there is a pattern of the variation range of the encoding ratio for each image. The satellite images have larger values of the encoding ratio than those of nature images (close range images). Depending on the wavelet pairs, the PSNR and the encoding ratio vary as much as 13.2 to 21.6% and 16.8 to 45.5%, respectively for each image. For Synthetic Aperture Radar (SAR) images the encoding ratio varies from 16 to 20% while for the nature images it varies more than 40% depending on the choice of wavelet pairs. The choice of wavelet for the compression affects the nature images more than the satellite images. With the indices such as the PSNR and the encoding ratio, the satellite images are less sensitive to the choice of wavelet pairs. A new index, energy concentration ratio (ECR) is proposed to investigate the effect of wavelet choice on the satellite image compression. It also shows that the satellite images are less sensitive than the nature images. Nevertheless, the effect of wavelet choice on the satellite image compression varies at least 10% for all three kinds of indices. However, the important of choice of wavelet pairs cannot be ignored.

키워드

참고문헌

  1. Cohen, A.I., Daubechies, I., and Feauveau, J.C., 1992, Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 45, 485-560. https://doi.org/10.1002/cpa.3160450502
  2. Daubechies, I., 1992, Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia, 357 p.
  3. Garca-Vlchez, F. and Serra-Sagrist, J., 2009, Extending the CCSDS recommendation for image data compression for remote sensing scenarios. Institute of Electrical and Electronics Engineers Transactions on Geoscience and Remote Sensing, 47, 3431-3445.
  4. Gladston, R., Revathy, K., and Raju, G., 2008, Study on the choice of wavelet filters for image compression using neural and k-nearest neighbor classifiers. Journal of Wavelet Theory and Applications, 2, 15-30.
  5. Grgic, S., Grgic, K., and Zovko-Cihlar, B., 2001, Performance analysis of image compression using wavelets. Institute of Electrical and Electronics Engineers Transactions on Industrial Electronics, 48, 682-695.
  6. Grossmann, A. and Morlet, J., 1984, Decomposition of hardy functions into square integrable wavelets of constant shape. Society for Industrial and Applied Mathematics on Mathematical Analysis, 15, 723-736.
  7. Jin, H., Yoo, H., Eom, J., Choi, I., and Han, D., 2009, Wavelet pair noise removal for increasing the classification accuracy of a remotely sensed image. Korean Journal of Remote Sensing, 25, 1-9. https://doi.org/10.7780/kjrs.2009.25.3.215
  8. Jin, H., Han, D., and Lee, H., 2010, Nearly optimal wavelet pairs for remotely sensed image compression. Proceedings of the 2010 Institute of Electrical and Electronics Engineers International Geoscience and Remote Sensing, 2179-2181.
  9. Jin, H. and Han, D., 2011, Choice of separable wavelets for image compression. Journal of Wavelet Theory and Applications. (to be appeared)
  10. Jung, H. and Lee, B., 2007, The S-wave velocity structure of shallow subsurface obtained by continuous wavelet transform of short period Rayleigh waves. Journal of the Korean Earth Science Society, 28, 903-913. https://doi.org/10.5467/JKESS.2007.28.7.903
  11. Keller, W., 2004, Wavelets in geodesy and geodynamics. Walter de Gruyter, NY, USA, 279 p.
  12. Kim, S., Jin, H., and Rim, H., 2004, Wavelet generation and it's application in gravity potential. Journal of the Korean Earth Science Society, 25, 256-264.
  13. Mallat, S., 1998, A wavelet tour of signal processing. Academic Press, USA, 577 p.
  14. Mandal, M.K., Panchanathan, S., and Aboulnasr, T., 1996, Choice of wavelets for image compression. Lecture Notes in Computer Science, 1133, 239-249. https://doi.org/10.1007/BFb0025147
  15. Oh, S., 2009, Variation analysis of geomagnetic data observed around the event of Andong earthquake (May 2, 2009). The Journal of the Korean Earth Science Society, 30, 683-691. https://doi.org/10.5467/JKESS.2009.30.6.683
  16. Rim, H., Jin, H., and Kwon, B., 1999, Denoise of synthetic and earth tidal effect using wavelet transform. Journal of the Korean Geophysical Society, 2, 143-152.
  17. Shapiro, J.M., 1993, Embedded image coding using zerotrees of wavelet coefficients. Institute of Electrical and Electronics Engineers Transactions on Signal Processing, 41, 3445-3462.
  18. Strang, G. and Nguyen, T., 1996, Wavelets and filter banks. Wellesley and Cambridge Press, MA, USA, 520 p.
  19. Thomos, N., Boulgouris, N., and Strintzis, M., 2006, Optimized transmission of JPEG2000 streams over wireless channels. Institute of Electrical and Electronics Engineers Transactions on Image Processing, 15, 54-67.
  20. Vetterli, M. and Kovacevic, J., 1995, Wavelets and subband coding. Prentice Hall, NJ, USA, 488 p.
  21. Villasenor, J., Belzer, B., and Liao, J., 1995, Wavelet filter evaluation for image compression. Institute of Electrical and Electronics Engineers Transactions on Image Processing, 4, 1053-1060.
  22. Wang, Z., Lu, L., and Bovik, A.C., 2004, Video quality assessment based on structural distortion measurement. Signal Processing: Image Communication, 19, 121-132. https://doi.org/10.1016/S0923-5965(03)00076-6
  23. Yoo, H.Y., Lee, K.W., Jin, H.S., and Kwon, B.D., 2008, Selecting optimal basis function with energy parameter in image classification based on wavelet coefficients. Korean Journal of Remote Sensing, 24, 437-444. https://doi.org/10.7780/kjrs.2008.24.5.437