The set partitioning in hierarchical trees (SPIHT) coding algorithm, proposed by Said and Pearlman, provides effective progressive and embedding property. However, for images with high energy that is randomly dispersed throughout high frequency subbands in the wavelet domain, the SPIHT does not fully exploit energy compaction of the wavelet transform and thus becomes less efficient to represent these images. This paper presents an energy compaction method, block reordering wavelet packet SPIHT (BRWP-SPIHT) coding, to enhance the image visual quality. The block reordering technique divides the wavelet coefficients into blocks and reorders these blocks based on the significance of each block. The simulation results show that BRWP-SPIHT is superior, on average, to SPIHT by 0.6 dB for texture rich images. Subjectively, it also shows significant enhancement to the quality of the reconstructed image, particularly for images with fractal and oscillatory patterns.

[1]A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning into hierarchical trees,” IEEE Trans. on Circuits and Systems for Video Technology, vol.6 pp.243-250, June 1996.
[2]J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Trans. Signal Processing, Spec. Issue Wavelets Signal Processing, vol. 41, pp. 3445-3462, Dec. 1993.
[3]S. A. Martucci, I. Sodagar, T. Chiang, and Y. Q. Zhang, "A Zerotree Wavelet Video Coder," IEEE Trans. on Circuits and System for Video Technology, vol. 7, no. 1, pp.109-118, Feb 1997.
[4]S. D. Servetto, K. Ramchandran and M. T. Orchard, “Wavelet based image coding via morphological prediction of significance,” in Proc. IEEE Int. Conf. Image Processing, pp. 530-533, Oct. 1995.
[5]S. D. Servetto, K. Ramchandran and M. T. Orchard, “Image coding based on a morphological representation of wavelet data,” IEEE Transactions on Image Processing, Vol. 8, pp. 1161-1174, Sept. 1999.
[6]B. B. Chai, J. Vass and X. Zhuang, “Significance-linked connected component analysis for wavelet image coding,” IEEE Transactions on Image Processing, Vol. 8, pp. 774-784, June 1999.
[7]R. M. Rao and A. S. Bopardikar, “Wavelet Transform,” 1998.
[8]A. Gersho and R. M. Gray, “Vector quantizatioin and signal compression,” Kluwer Academic Publishers. Feb. 1992.
[9]I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Communications of the ACM, Vol. 30, No. 6, Jun. 1987.
[10]D. A. Huffman, “A method for the construction of minimum-redundancy codes,” Proc. Inst. Electr. Radio Eng. 40, pp. 1098-1101, Sep. 1952.
[11]M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, vol. 1, pp. 205-220, Apr. 1992.
[12]http://www.ipl.rpi.edu/research/SPIHT/
[13]S. G. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern. Analysis and Machine Intell., vol. 11, pp. 674-693, July 1989.
[14]R. R. Coifman and M. V. Wickerhauser, "Entropy-based algorithms for best basis selection," IEEE Trans. Information Theory, vol. 38, pp. 713-718, March, 1992.
[15]D. Taubman, “High performance scalable image compression with EBCOT, ” Proce. IEEE Int. Conference on Image Processing(ICIP), pp. 24-28, October 1999, Kobe, Japan.
[16]D. Taubman, “High performance scalable image compression with EBCOT, ” IEEE Transactions on Image Processing, Vol. 9, pp. 1158-1170, July. 2000.
[17]F. G. Meyer, A. Z. Averbuch, “Fast adaptive wavelet packet image compression,” IEEE Transactions on Image Processing, Vol. 9, pp. 792-800, May 2000.
[18]N. M. Rajpoot, F. G. Meyer, R. G. Wilson and R. R. Coifman, “On zerotree quantization for embedded wavelet packet image coding,” in Proc. Int. Conf. Image Processing, 1999.