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研究生:林志鴻 
研究生(外文):Jyh-Horng Lin
論文名稱:嵌入式傳真伺服器設計與小波壓縮於此系統中的應用
論文名稱(外文):The Design of Embedded Fax Server and The Application of Wavelet in the System
指導教授:廖俊睿
指導教授(外文):Jan-Ray Liao
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:82
中文關鍵詞:G3G4MHMRMMR傳真小波轉換上下文模型算術編碼
外文關鍵詞:G3G4MHMRMMRfaxwavelet transformcontext modelingarithmetic coding
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傳真伺服器整合傳真機與網路的特點,可以將傳真接收的文件以夾檔的方式寄到管理者的E-Mail信箱,使用者可以事先預覽文件後再決定是否列印,兼具省紙環保的功能,適用於家庭、學校和公司等地,使得傳真功能更加的普及。
影像壓縮為傳真機高速傳送時不可或缺的技術,CCITT所公布之G3、G4傳真機的編碼方式MH(Modified Huffman)、MR(Modified Read)、MMR(Modified MR)是在空間領域上作壓縮,是根據統計結果設計出的編碼表作編碼來達到壓縮目的,故其壓縮效果對於一般的文件可以說相當的良好,但是對於圖形的文件就不是很理想,因此將針對這方面來加以改善。
本論文使用小波壓縮法來對傳真文件作壓縮編碼,首先將影像作小波轉換,使得能量較原來訊號來得集中;接著使用上下文模型進行分類,盡量讓每類出現狀況的機率分佈偏差很大;最後使用算術編碼來作壓縮,希望能在頻率領域上作壓縮來得到比目前壓縮標準更好的壓縮效果。
從實驗結果得知,在處理一般文件時,小波壓縮法與G3編碼的壓縮比相近,在處理表格時會稍比G3編碼來得好,雖然都比不上G4的編碼方式,但在處理圖形時則遠優於G3、G4編碼,如欲在文字與圖形編碼間找一平衡點時,小波壓縮法實是不錯的選擇。
A fax server integrates the functionality of a fax machine into network. It can receive fax, then e-mail the received documents to administrator’s mail box by attaching the files into the mail. Users can preview the documents, then decide whether to print it or not. It is both environment friendly and economical. It can be used in families, schools and companies, and make fax more popular.
Image compression is a crucial part of the fax process. The coding modes published by CCITT was G3 and G4. They used MH(Modified Huffman), MR(Modified Read) and MMR(Modified MR). All of them compress in spatial domain. The compression is achieved by encoding the scanned documents using predefined table determined by statistics in text documents. The compression ratio is generally good for text documents but it is not suitable for graphics and images. Therefore, we will improve it in the thesis.
The thesis uses wavelet transform to compress the fax documents. First, we use wavelet to transform the images. Then, we use context modeling to classify the transformed images. The goal of the classification is to make the probability in each class biases to a certain range. Finally, we use arithmetic coding to encode the images. We expect that it can achieve better compression ratio than the fax standard.
In the experiments, when we process general text documents, the compression ratio of wavelet is close to G3 encoding. When we process tables, the compression ratio of wavelet is better than G3 encoding. Although it’s not better than G4 encoding in texts or tables, it’s better than G3 and G4 encoding in graphics and images. We believe that using wavelet in fax compression is a good choice because it achieves a good balance between texts and images.
第一章 緒論....................................................1
1.1 簡介.......................................................1
1.2 動機與目的.................................................2
1.3 論文架構...................................................3
第二章 傳真系統................................................4
2.1 傳真系統的基本原理.........................................4
2.2 傳真系統的規格.............................................5
2.3 傳真系統的作業程序.........................................7
2.3.1 傳真程序控制.............................................7
2.3.2 傳真作業流程.............................................9
2.3.3 傳真控制-HDLC..........................................10
2.3.4 傳真/數據機Class 1命令簡介.............................13
第三章 傳真資料壓縮...........................................19
3.1 哈夫曼(HUFFMAN)編碼.....................................19
3.2 傳真系統的壓縮編碼........................................23
3.2.1 MH(Modified Huffman)編碼..............................24
3.2.2 MR(Modified Read)編碼.................................31
3.2.3 MMR(Modified MR)編碼..................................36
3.3 算術(ARITHMETIC)編碼....................................37
第四章 嵌入式傳真伺服器設計...................................44
4.1 傳真伺服器的標準..........................................44
4.2 傳真伺服器的設計..........................................46
第五章 小波轉換...............................................49
5.1 小波轉換的特性............................................49
5.2 HAAR函數離散小波轉換的原理................................50
5.3 離散小波轉換的實行........................................55
第六章 小波壓縮法及其實驗結果.................................60
6.1 上下文模型求得的分類標準..................................61
6.2 分類和編碼................................................73
6.3 測試結果..................................................75
第七章 結論與未來展望.........................................79
[1] 陳宗義, 傳真機-FAX, 全華科技圖書股份有限公司, 1992.
[2] 湯鴻沼, 通訊系統, 全華科技圖書股份有限公司, 1991.
[3] 李明昌, 影像壓縮技術與應用, 全華科技圖書股份有限公司, 1998.
[4] 繆紹綱, 數位影像處理-活用Matlab, 全華科技圖書股份有限公司, 1999.
[5] 單維彰, 凌波初步, 全華科技圖書股份有限公司, 1999.
[6] 葉光釗, Fax/Modem傳真程式設計, 益眾資訊有限公司, 1994.
[7] 戴顯權, 資料壓縮, 松崗電腦圖書資料股份有限公司, 2000.
[8] 連國珍, 數位影像處理, 儒林圖書有限公司, 1992.
[9] 余松煜, 周源華, 吳時光, 數位影像處理, 儒林圖書有限公司, 1993.
[10] Andrew Margolis, “The Fax Modem Sourcebook”, JOHN WILEY & SONS, 1995.
[11] Khalid Sayood, “Introduction to Data Compression”, Morgn Kaufmann Publishers, 2000.
[12] C. Chrysafis and A. Ortega, “Line-Based, Reduced Memory, Wavelet Image Compression”, IEEE Trans. on Image Processing, vol. 9, pp. 378-389, Mar. 2000.
[13] C. Chrysafis and A. Ortega, “Line-based reduced memory wavelet image compression,” in Proc. IEEE Data Compression Conf., Snowbird, UT, pp. 308—407, 1998.
[14] M. Weinberger, G. Seroussi, and G. Sapiro, “Loco─I: A low com-plexity, context-based, lossless image compression algorithm,” in Proceedings of the IEEE Data Compression Conference. Los Alamitos, CA: IEEE Comput. Soc. Press, 1996.
[15] R. L. Joshi, H. Jafarkhani, J. H. Kasner, T. R. Fischer, N. Farvardin, M. W. Marcellin, and R. H. Bamberger, “Comparison of different methods of classification in subband coding of images,” IEEE Trans. Image Pro-cessing, vol. 6, pp. 1473—1486, Nov. 1997.
[16] Y. Yoo, A. Ortega, and B. Yu, “Image subband coding using progres-sive classification and adaptive quantization,” IEEE Trans. Image Pro-cessing, pp. 1702—1715, Dec. 1999.
[17] WITTEN, I. H., NEAL, R. M. and CLEARY, J. G., “Arithmetic Coding for Data Compression. ”, Commun. ACM 30, 6 (1987), 520—540.
[18] G. G. Langdon, Jr., Senior member, IEEE and Jorma Rissanen, “Compression of Black-White Images with Arithmetic Coding”, IEEE Trans. on Communications, vol. COM-29, NO. 6, June 1981.
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