在本篇論文中，我們將提出一種以資料的結構性與預測準確度做為預測資料的基礎，利用不甚精確的字典紀錄，使其能有效地預測下一筆資料的可能範圍。藉由概略性的模型，使預測的資料儘可能匹配真實資料，以達到提高資料的冗餘性，縮減資料之間的Entropy，進而輔助其它壓縮演算法來提高其壓縮比，達到簡化資料，增進儲存空間的利用率。

We propose an algorithm according to the data sequence and the degree of accuracy of guess to predict bmp type data in lossless data compression in our research.

We research the correlation between two pixels. And our flow of prediction can decrease the entropy of data and aid algorithm of compression to increase its compression ratio.

[1] 鍾國亮, “影像處理與電腦視覺, 3/e”, 台北：臺灣東華, 1996
[2] 陳偉銘, 趙涵捷, “影像裡的數學世界”, 台北：臺灣書店, 1999
[3] Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing, 2/e”, 台北：台灣培生, 2003
[4] 昌達K’ z, “壓縮演算法：編碼原理與使用C語言的實作”, 台北：全華圖書, 2005
[5] Khalid Sayood, “Introduction to data compression, 3/e”, 台北：全華圖書, 2006
[6] C.E. Shannon, “A Mathematical Theory of Communication”, Bell System Technical Journal, vol. 27, pp. 379-423, 623-656, July, October, 1948
[7] A. Habibi, “Comparison of nth-Order DPCM Encoder with Linear Transformations and Block Quantization Techniques”, IEEE Trans. Comm. Tech., vol. COM19, no. 6, pp. 948-956, December 1971
[8] B. Aiazzi, P.S. Aiba, L. Alparone, S. Baronti, “Reversible Compression of Hyper-Spectral Imagery through Inter-Band Fuzzy Prediction and Context Coding”, Geoscience and Remote Sensing Symposium Proceedings, vol. 5, pp. 2685-2687, July 1998
[9] B. Aiazzi, S. Baronti, L. Alparone, “Lossless Image Compression Based on A Fuzzy-Clustered Prediction”, Circuits and Systems, vol. 4, pp. 9-12, 30 May-2, June, 1999
[10] N.A. El-Yamany, “A New Fuzzy Gradient-Adaptive Lossy Predictive Coding System for Still Image Compression”, EUROCON 2003. Computer as a Tool. The IEEE Region 8, vol. 2, pp. 432-436, September, 2003
[11] José Luis Crespo, Marta Zorrilla, Pilar Bernardos, Eduardo Mora, “A New Image Prediction Model Based on Spatio-Temporal Techniques”, The Visual Computer, vol. 23, Number 6, pp. 419-431(13), June 2007
[12] Salah Ameer, Otman Basir, “Image Compression Using Plane Fitting With Inter-Block Prediction”, Image and Vision Computing, vol. 27, pp. 385-390, March 2009
[13] D. A. Huffman, “A Method for The Construction of Minimum Redundancy Codes”, Proc. IRE, 40, pp. 1098-1101, September 1952
[14] B. W. Y. Wei, T. H. Meng, “A Parallel Decoder of Programmable Huffman Codes”, Circuits and Systems for Video Technology , IEEE Transactions, vol. 5, pp. 175-178, April 1955
[15] K. L. Chung, J. G. Wu, “Level-Compressed Huffman Decoding”, Communications, IEEE Transactions, vol.47, pp. 1455-1457, October 1999
[16] G. N. N. Martin, “Range Encoding: An Algorithm for Removing Redundancy from A Digitised Message”, Video & Data Recording Conference, pp. 24-27, July 1979
[17] G. G. Langdon, Jr., “An Introduction to Arithmetic Coding”, IBM J. of Research and Development, vol. 28, March 1984
[18] I. H. Witten, Jr., R. M. Neal, J. G. Cleary, “Arithmetic Coding for Data Compression”, Communications of the ACM archive, vol. 30, pp. 520-540, June 1987
[19] Amir Said, “Introduction to Arithmetic Coding - Theory and Practice”, Imaging Systems Laboratory, HP Laboratories Palo Alto, April 2004
[20] J.G. Cleary, W.J. Teahan, I.H. Witten, “Unbounded length contexts for PPM”, Data Compression Conference, pp. 52-61, March 1995
[21] C. Bloom., “Solving the Problems of Context Modeling”, 1998
[22] D. Shkarin, “PPM: one step to practicality”, Data Compression Conference, pp. 202-211, April 2002
[23] Jukka Teuhola, Timo Raita, “Text compression using prediction”, Annual ACM Conference 9th, pp. 97-102, 1986
[24] E. Federovsky, M. Feder, S. Weiss, “Branch Prediction Based on Universal Data Compression Algorithms”, IEEE on The 25th Annual International Symposium, pp. 62-72, July 1998