在本論文中，我們提出了一個無失真帶狀衛星影像壓縮法，使用可變式編碼區塊大小的JPEG2000。對於一般自然影像而言，JPEG2000預設的區塊大小可以達到不錯的效果，但是預設的區塊大小並不適合於帶狀衛星影像的資料。為了符合帶狀衛星影像的特性，我們將運用我們所提出的演算法來找到適合的編碼區塊大小。首先，我們會計算各個頻帶的能量並決定目前輸入資料屬於哪個模式，在根據不同模式，給定不同的編碼方塊大小。在決定完模式之後，我們會進一步去判斷水平和垂直邊緣的能量，根據判斷結果，再調整編碼方塊大小。最後，我們會將所決定的編碼方塊大小當作JPEG2000的輸入參數。從實驗結果得知，不同帶狀衛星影像使用不
同的編碼方塊大小可以得到比較好的壓縮倍率。

In this thesis, a lossless strip-based satellite image compression by using variable code block sizes is proposed. For the standard nature image, the default code block size is fine. As for a strip, the default code block size seems not suitable for the input data. To fit the feature of the strip-based images of satellites, we will find the better code block size by our proposed method. First, we will calculate the energy of different bands and decide whattype of the code block size is good for the current strip. After determining the type of the strip, we will further check the energy in the horizontal and vertical direction. According to the result, we will adjust the code block size again. Lastly, the final code block size will be an input variable for JPEG2000. From the experiment results, according to different input data, using different code block size takes a better compression rate.

[1] Ahmed Abu-hajar and Ravi Sankar. Wavelet based lossless compression using partial spiht and bit plane based arithmetic coding. IEEE, 2002.
[2] Jicheng An and Zixing Cai. Efficient rate control for lossless mode of jpeg2000. IEEE Signal Processing Letters, 2008.
[3] R. L. Baker and Y. T. Tse. Compression of high spectral resolution imagery. Proc. SPIE, 1988.
[4] Raghunadh K. Bhattar, K. R. Ramakrishnan, and K. S. Dasgupta. Strip based coding for large images using wavelets. Signal Processing: Image Communication, Elsevier, 2002.
[5] M. Cagnazzo, S. Parrilli, G. Poggi, and L. Verdoliva. Improved class-based coding of multispectral images with shape-adaptive wavelet trans-form. IEEE Geoscience and Remote Sensing Letters, 2007.
[6] Li Wern Chew, Li-Minn Ang, and Kah Phooi Seng. New virtual tree structure for very low memory strip-based image compression. IEEE Signal Processing Letters, 2008.
[7] X. Delaunay, M. Chabert, G. Morin, and V. Charvillat. Bit-plane analysis and contexts combining of jpeg2000 contexts for on-board satellite image compression. IEEE, 2007.
[8] G. F. Fahmy and S. Panchanathan. A lifting based system for optimal compression and clasification in the jpeg2000 framework. IEEE, 2002.
[9] Hung-Chi Fang, Yu-Wei Chang, Chih-Chi Cheng, CHun-Chia Chen, and Liang-Gee Chen. Memory efficient jpeg2000 architecture with stripe pipeline scheme. ICASSP, 2005.
[10] Fouad Khelifi, Ahmed Bouridance, and Fatih Kurugollu. Joined spectral trees for scalable spiht-based multispectral image compression. IEEE Transaction on Multimedia, 2008.
[11] Hariharan G. Lalgudi, Ali Bilgin, Michael W. Marchellin, and Mariap-pan S. Nadar. Compression of multidimensional images using jpeg2000. IEEE Signal Processing Letters, 2008.
[12] Hua Li and Yiming Zhu. Lossless image compression based on dpcm-iwpt. ISECS, 2008.
[13] T. T. Lu and P. C. Chang. Significant bit-plane clustering technique for jpeg2000 image coding. Electronics Letters, 2004.
[14] S. Mallat. A wavelet tour of signal processing. CA:Academic, 1997.
[15] K. R. Rao and P. Yip. Discrete cosine transform: Algorithms, advantages and applications. New York: Academic, 1990.
[16] Amir Said and William A. Pearlman. A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transaction on Circuits and Systems for Video Technology, 1996.
[17] D. S. Taubman and M. Marcellin. Jpeg2000-image compression fundamentals, standard and practice. Springer, 2002.
[18] Michael Unser and Thierry Blu. Mathematical properties of the jpeg2000 wavelet filters. IEEE Transactions on Image Processing, 2003.
[19] M. Weinberger, G. Seroussi0, and G. Sapiro. Loco-i: A low complexity, context-based, lossless image compression algorithm. Proc. IEEE Data Compression Conference, 1996.
[20] Sehoon Yea and William A. Pearlman. A wavelet-based two-stage near-lossless coder. IEEE Transaction on Image Processing, 2006.
[21] Wen-Chien Yen and Yen-Yu Chen. Natural image compression based on modified spiht. ICIS, 2005.