REAL-TIME IMAGE WAVELET CODING FOR LOW BIT RATE TRANSMISSION

Gaoyong Luo

Abstract

Embedded coding for progressive image transmission has recently gained popularity in image compression community. However, current progressive wavelet-based image coders tend to be complex and computationally intense requiring large memory space. The encoding process usually sends information on the lowest-frequency wavelet coefficients first. At very low bit rates, images compressed are therefore dominated by low frequency information, where high frequency components belonging to edges are lost leading to blurring the signal features. This paper presents a new image coder for real-time transmission, employing edge preservation based on local variance analysis to improve the visual appearance and recognizability of compressed images. The analysis and compression is performed by dividing an image into blocks. Lifting wavelet filter bank is constructed for image decomposition and reconstruction with the advantages of being computationally efficient and boundary effects minimized. A modified SPIHT algorithm with more bits used to encode the wavelet coefficients and transmitting fewer bits in the sorting pass for performance improvement, is used to reduce the correlation of the coefficients at scalable bit rates. Local variance estimation and edge strength measurement can effectively determine the best bit allocation for each block to preserve the local features. Experimental results demonstrate that the method performs well both visually and in terms of quantitative performance measures, and offers error resilience feature that is evaluated using a simulated transmission channel with random error.

References

  1. Adams, M.D., 2002. The JPEG-2000 Still Image Compression Standard, ISO/IEC JTC 1/SC 29/WG 1 N 2412.
  2. Alatan, A.A., Zhao, M., and Akansu, A.N., 2000. Unequal error protection of SPIHT encoded image bit streams,” IEEE Journal on Selected Areas in Communications, Vol. 18, No. 6, pp. 814-818.
  3. Chang, S., Yu, B., and Vetterli, M., 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, Vol. 9, No. 9, 1532-1546.
  4. Claypoole, Davis, G.M., Sweldens, W., and Baraniuk, R., 2003. Nonlinear wavelet transforms for image coding via lifting. IEEE Transactions on Image Processing, Volume: 12, Issue: 12, pp 1449-1459.
  5. Daubechies, I., and Sweldens, W., 1998. Factoring wavelet transforms into lifting steps. J. Fourier Anal. Appl. 4(3), 247-269.
  6. de Queiroz, R., Nguyen, T.Q., and Rao, K., 1996. The GenLOT: generalized linear-phase lapped orthogonal transform. IEEE Transactions on Signal Processing, vol. 40, pp. 497-507.
  7. Hilton, M.L., Jawerth, B.D., and Sengupta, A., 1994. Compressing still and moving images with wavelets. Multimedia Systems, Volume 2, Issue 5, pp 218-227.
  8. Lay, K.-T., and Wang, L.-J., 2005. Image coding with optimal wavelet packet and greedy edge enhancement. Proceedings of the First International Conference on Systems and Signals (ICSS), Kaohsiung, Taiwan.
  9. Li, J., and Lei, S., 1999. An embedded still image coder with rate-distortion optimization. IEEE Transactions on Image Processing, Vol. 8, No. 7, pp 913-924.
  10. Malvar, H.S., 1992. Signal Processing with Lapped Transforms, Norwood, MA, Artech House.
  11. Meyer, F.G., Averbuch,, A.Z., and Coifman, R.R., 2002. Multilayered image representation: application to image compression. IEEE Transactions on Image Processing, Vol. 11, No. 9, pp 1072-1080.
  12. Pearlman, W.A., 2001. Trends of tree-based, set partitioning compression techniques in still and moving image systems. Proceedings of 22nd Picture Coding Symposium (PCS-2001), Seoul, Korea, pp 1-8, 25-27.
  13. Pearlman, W.A., Islam, A., Nagaraj, N., and Said, A., 2004. Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, pp 1219-1235.
  14. Rajpoot, N.M., Wilson, R.G., Meyer, F.G., and Coifman, R.R., 2003. Adaptive wavelet packet basis selection for zerotree image coding. IEEE Transactions on Image Processing, Vol. 12, No. 12, pp 1460-1472.
  15. Saha, S., and Vemuri, R., 2000. An analysis on the effect of image features on lossy coding performance. IEEE Signal Processing Letters, Volume: 7, pp 104-107.
  16. Said, A., and Pearlman, W., 1996. A new, fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 6, pp 243-250.
  17. Schilling, D., and Cosman, P.C., 1999. Edge-enhanced image coding for low bit. Proceedings of International Conference on Image Processing (ICIP 7899), Volume III, Kobe, Japan, IEEE Computer Society, pp 747-751.
  18. Shapiro, J., 1993. Embedded image coding using zerotree of wavelet coefficients. IEEE Transactions on Signal Processing, Vol. 41, pp 3445-3462.
  19. Sweldens, W., 1996. The lifting scheme: A custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon. Anal. 3(2), 186-200.
  20. Sweldens, W., 1998. The lifting scheme: A construction of second generation wavelets. SIAM J. Math. Anal. 29(2), 511-546, 1998.
  21. Taubman, D., 2000. High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, Vol. 9, No. 7, pp 1158-1170.
  22. Vargic, R., 1998. An approach to 2D wavelet transform and its use for image compression. Radioengineering, Vol. 7, No. 4, 1-6.
  23. Vetterli, M., and Kovacevic, J., 1995. Wavelets and Subband Coding, Englewood Cliffs, NJ, Prentice Hall.
  24. Yang, S.H., and Cheng, T.C., 2000. Error-resilient SPIHT image coding. Electron. Lett., vol.36, no. 3, pp. 208- 210.
  25. Ye, S., Sun, Q., and Chang, E., 2004. Edge directed filter based error concealment for wavelet-based images. Proceedings of International Conference on Image Processing (ICIP 7804), Singapore, Vol. 2, pp 809- 812.
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Paper Citation


in Harvard Style

Luo G. (2006). REAL-TIME IMAGE WAVELET CODING FOR LOW BIT RATE TRANSMISSION . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006) ISBN 978-972-8865-64-1, pages 157-163. DOI: 10.5220/0001569901570163


in Bibtex Style

@conference{sigmap06,
author={Gaoyong Luo},
title={REAL-TIME IMAGE WAVELET CODING FOR LOW BIT RATE TRANSMISSION},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006)},
year={2006},
pages={157-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001569901570163},
isbn={978-972-8865-64-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2006)
TI - REAL-TIME IMAGE WAVELET CODING FOR LOW BIT RATE TRANSMISSION
SN - 978-972-8865-64-1
AU - Luo G.
PY - 2006
SP - 157
EP - 163
DO - 10.5220/0001569901570163