近來的研究發展顯示基於小波轉換的影像壓縮技術提供了傳統影像壓縮技術所沒有的優點，例如在漸進式傳輸、壓縮比，以及頻寬利用性等均較傳統技術來的佳。由Shapiro所提出的嵌入式零樹編碼法(EZW)以及由Said and Pearlman所提出的分集階層樹編碼法(SPIHT)均展示出以小波轉換為基礎之影像壓縮具競爭力的效能。又最新一代的靜態影像壓縮標準JPEG2000，在其演算法內也有採用小波轉換;然而，JPEG2000的複雜度較上述兩種編碼法來的高。
在本篇論文裡，我們針對無算術編碼之SPIHT提出區塊內涵式算術編碼來增進其編碼效能。我們所提出的方法平均較無算術編碼之SPIHT於PSNR上約增加0.2~0.7dB，並且此方法也可以應用到其他的零樹編碼法。

Recent research advances have shown that wavelet-based image compression techniques offer several advantages over traditional techniques in terms of progressive transmission capability, compression efficiency, and bandwidth utilization. The embedded zero-tree wavelet (EZW) coding technique suggested by Shapiro, and its modification—set partitioning in hierarchical trees (SPIHT), suggested by Said and Pearlman—demonstrate the competitive performance of wavelet based compression schemes. Even the new JPEG2000 image coding standard, employ a wavelet transform in its algorithm. However, the complexity of JPEG2000 standard is higher than the zero-tree coding methods.
In this thesis, we focus on the SPIHT algorithm and proposing a suitable block context-based arithmetic codes for improving the SPIHT coders. The proposed arithmetic codes are shown to yield PSNR improvements averaging 0.2~0.7 dB and are applicable to other zero-tree coding methods

[1] W. Pennebaker and J. Mitchell, JPEG Still Image Data Compression
Standard. New York: Van Nostrand Reinhold, 1993.
[2] Coding of Moving Pictures and Associated Audio for Digital Storage Media up to Sbout 1.5 Mbit/s, Tech. Rep., ISO/IEC IS 11 172(MPEG-1), 1993.
[3] Generic Coding of Moving Pictures and Associated Audio, Tech. Rep., ISO/IEC DIS 13 818(MPEG-2), 1994.
[4] L. Chiariglione, “MPEG and multimedia communications,” IEEE Trans.Circuits Syst. Video Technol., vol. 7, pp. 5–18, Feb. 1997.
[5] T. Sikora, “The MPEG-4 video standard verification model,” IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 19–31, Feb. 1997.
[6] F. Pereira and T. Alpert, “MPEG-4 video subjective test procedures and results,” IEEE Trans. Circuits Syst. Video Technol., vol. 7, pp. 32–51, Feb. 1997.
[7] “Video codec for audiovisual services at p x 64 kb/s,”, ITU-T Rec. H.261, 1990.
[8] Video Coding for Low Bit Rate Communications, ITU-T Draft Rec. H.263, Dec. 1995.
[9] J. Shapiro, “Embedded image coding using zerotree of wavelet coefficients,” IEEE Trans. Signal Processing, vol. 41, pp. 3445–3463, Dec. 1993.
[10] A. Said andW. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst. Video Technol., vol. 6, pp. 243–250, June 1996.
[11] JPEG 2000 verification model 7.2, ISO/IEC JTC1/SC29/WG1, May 2000.
[12] D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Trans. Image Processing, vol. 9, pp. 1158–1170, July
[13] Daubechies. “Orthonormal bases of compactly supported wavelets.” Commun. Pure Appl. Math. Vol. 41. pp. 909-996. Nov. 1988.
[14] G. Mallat, “A theory for multiresolution signal decomposition : The Wavelet Representation,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 11, NO. 7, July 1989.
[15] I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Commun. ACM, vol. 30, no. 6, pp. 520–540, June 1987.
[16] D. Taubman and Michael W.Marcellin “JPEG2000 image compression fundamentals, standards, and practice”
[17] D. Taubman and A. Zakhor, “Multirate 3-D Subband Coding of Video,” IEEE Trans. Image Processing, 3(5):572–588, Sept. 1994.
[18] P. E. Tischer, R. T. Worley, A. J. Maeder, and M. Goodwin, “Contextbased lossless image compression,” Comput. J., vol. 36, no. 1, 1993.
[19] E. L. Schwartz, A. Zandi, and M. Boliek, “Implementation of compression with reversible embedded wavelets,” in Proc. SPIE, vol. 2564, 1995.
[20] N. Memon, X. Wu, and B. L. Yeo, “Entropy coding techniques for lossless image compression with reversible integer wavelet transforms,” IBM , Res. Rep. RC21010, Oct. 22, 1997.
[21] X.Wu and N. Memon, “Context-based, adaptive, lossless image codec,” IEEE Trans. Commun., vol. 45, pp. 437–444, Apr. 1997.
[22] C. Chrysafis and A. Ortega, “Efficient context-based entropy coding for lossy wavelet coefficients image compression,” in Proc. DCC, Mar.1997, pp. 241–250.
[23] Ramaswamy, V.N. and Namuduri, K.R. and Ranganathan, N., “Context based lossless intraframe coding of video sequence using embedded zerotree wavelets,” in Proc. Int. Symp. Circuits and Systems, May–June 1999.
[24] V. N. Ramaswamy, “Lossless image compression using wavelet decomposition,” Ph.D dissertation, Dept. Comput. Sci. Eng., Univ. of South Florida, Tampa, FL, Aug. 1998.
[25] N. V. Bougloris and M. G. Strinzis, “Reversible multiresolution image coding based on adaptive lifting,” Proc. IEEE Int. Conf. Image Processing, Oct. 24–28, 1999.
[26] M. D. Adams and F. Kossentini, “Reversible integer-to-integer wavelet transforms for image compression: Performance evaluation and analysis,” IEEE Trans. Image Processing, vol. 9, pp. 1010–1024, June 2000.
[27] G. Minami, Z. Xiong, A. Wang, and S. Mehrotra, “3-D wavelet coding of video with arbitrary regions of support,” IEEE Trans. Circuits Syst. Video Technol., vol. 11, pp. 1063–1068, Sept. 2001.
[28] E. Atsumi and N. Farvardin, “Lossy/lossless region-of-interest image coding based on set partitioning in hierarchical trees,” in Proc. IEEE Int. Conf. Image Processing (ICIP-98), Chicago, IL, Oct. 1998.
[29] D. S. Crus, T. Ebrahimi, M. Larsson, J. Askelöf, and C. Christopoulos, “Region of interest coding in JPEG2000 for interactive client/sever applications,” in Proc. IEEE 3rd Workshop on Multimedia Signal Processing, 1999, pp. 389–394.
[30] C. Christopoulos, J. Askelöf, and M. Larsson, “Efficient region of interest coding techniques in the upcoming JPEG2000 still image coding standard,” in Proc. IEEE Int. Conf. Image Processing (ICIP-2000), Vancouver, Canada, Sept. 2000.