臺灣博碩士論文加值系統

從小波發展至今，傳統二維離散雙正交小波轉換的超大型積體電路架構都是以遞迴式金字塔演算法為基礎。在此理論基礎上的架構都必須利用插值法的技術去解決因回授而造成的多重資料輸入衝突，如此將會大大的增加電路的複雜度和資料的硬體運算時間。為了避免如此的問題，本篇論文針對分離式的二維離散小波轉換提出了一個有效率的二維串聯式分離式演算法之超大型積體電路架構。此二維架構是建構在一個有效率的一維分離式演算法架構，此一維的架構是根據分離式演算法的資料流程分析而建立的，在文章中我們會介紹分離式演算法架構的優點並和傳統的濾波器架構做比較。之後我們使用Altera Max+plus II來完成一維架構的模擬，並設計出二維的架構。

Since the traditional VLSI architectures of the 2D discrete biorthogonal wavelet transform (DBWT) are based on the recursive pyramid algorithm. They need to apply the interleaving technique to solve the confliction problem of multi-level data input. This will increases circuit complexity and time latency. To avoid from the difficulty, this paper develops an efficient cascaded 2-D lifting VLSI architecture for separable 2-D DBWT. The construction of 2-D architecture is based on an efficient 1-D lifting architecture which is constructed by the data flow graph analysis of a modified lifting algorithm. We show the advantages offered by lifting scheme, compared to classical filter banks based architecture. The 1-D architecture is completely simulated on Altera Max+plus II. The 2-D architecture is designed.

中文摘要..................................................I
英文摘要..................................................III
致謝......................................................IV
目錄......................................................V
圖目錄....................................................VII
表目錄....................................................X
第一章 序論...............................................1-1
1-1研究動機和背景.........................................1-1
1-2 小波轉換(Wavelet Transform)的背景.....................1-2
1-3雙正交小波轉換(BWT)的重要性及優點......................1-7
1-4分離式演算法(Lifting)的優點............................1-8
1-4-1 各種不同1D Lifting方式的比較回顧....................1-10
1-5 本文之Overview........................................1-14
第二章 小波轉換(Wavelet Transform)演算法之回顧............2-1
2-1各種實現WT方法的文獻回顧...............................2-1
2-2小波的理論探討.........................................2-3
2-2-1串級法(cascading method)............................2-5
2-2-2碎形內插數演算法.....................................2-7
2-3線性代數上的觀念.......................................2-8
2-4多重解析度分析(MRA)....................................2-11
2-5二尺度函數(two-scale relations)........................2-13
2-6小波的正交、半正交、平移正交與雙正交性.................2-17
2-7離散小波轉換(Discrete Wavelet Transform )..............2-20
2-8分離式演算法系統(Lifting System).......................2-24
第三章 1D超大型積體電路架構...............................3-1
3-1 1D小波轉換概念........................................3-1
3-2 一維9/7 BWT Filter bank超大型積體電路架構.............3-4
3-3 1D 9/7 BWT分離式演算法之超大型積體電路架構............3-10
3-3-1 1D Pipeline 9/7 BWT分離式演算法之超大型積體電路構...3-16
3-3-2 Split硬體架構.......................................3-21
3-3-3 MAC硬體架構.........................................3-23
第四章 二維的超大型積體電路架構...........................4-1
4-1二維影像壓縮概念.......................................4-1
4-2一階二維9/7 BWT分離式演算法之超大型積體電路架構........4-4
4-3 Split和Lifting列運算架構..............................4-8
4-4 Memory及Counter架構...................................4-10
4-5 濾波電路架構..........................................4-12
4-6 Split和Lifting行運算架構..............................4-14
4-7 三階二維9/7 BWT Lifting超大型積體電路架構.............4-16
第五章 模擬驗證與效能分析.................................5-1
5-1 數位設計概念及流程....................................5-1
5-2 數位邏輯晶片分類......................................5-3
5-3 Altera MAX+plusII 設計流程............................5-4
5-4 二維BWT Lifting Bit數之推估...........................5-6
5-5 9/7 BWT係數分析.......................................5-10
5-6 二維Row/Column Split硬體實現及效能分析................5-11
5-7 Lifting 列/行運算的硬體實現及效能分析.................5-14
5-8 Scaling及Lifting列/行運算的硬體實現...................5-18
5-9 Memory硬體實現和效能分析..............................5-20
5-10 濾波電路硬體實現及效能分析...........................5-22
5-11 二維BWT Lifting超大型積體電路架構....................5-28
5-12 二維硬體架構比較.....................................5-39
5-13 Demo Board 下載驗證..................................5-41
第六章 結論與未來發展.....................................6-1
參考文獻

[1] S. Mallat, ”A Theory for Multiresolution Signal Decomposition : The Wavelet Representation,” IEEE Trans. On Pattern Analysis and Machine Intell, vol , 11 , No.7, pp.674-693 , July 1989.
[2] Hiyama , S. and Kobayashi, M. ,(1998) , “Wavelet-based multiresolution display of coastline data “ pp . 1-11
[3]王鴻傑, “小波基底函數產生系統之設計與實作, “國立中正大學化學工程研究所 , 八十九學年度碩士論文.
[4]鍾瑛靜, “小波理論在質點運動影像之評估, ”國立台北科技大學電機整合研究所 , 九十學年度碩士論文
[5]劉震昌, “小波轉換應用於二維碎形布朗運動估測, 抗雜訊之影像壓縮, 及影像序列上物件追蹤,”國立台灣大學電機工程研究所, 九十學年博士論文
[6]繆邵剛, “數位影像處理,活用-Matlab,” 全華科技圖書股份有限公司, 88年11月第二版
[7]李慶建, “衛星影像融合技術之比較與分析,” 國防大學中正理工學院電子工程研究所, 八十九學年碩士論文
[8]洪志偉, “基於類神經模糊系統及小波轉換於數位灰階影像浮水印技術設計,” 國立中正大學通訊工程研究所, 八十九學年碩士論文
[9]許巍嚴,” 以多解析小波轉換為基礎的顯微影像接合系統，”國立成功大學資訊工程研究所, 八十九學年碩士論文
[10] 許育嘉, “小波神經網路於土木工程之應用,”國立交通大學土木工程系, 八十九學年碩士論文
[11] A. Said and W. A. Peariman , “An image multiresolution representation for lossless and lossy compression, “ IEEE Trans. Image Processing, vol.5, pp. 1303-1310, Sept. 1996.
[12] M. J. Gormish, E. L. Schwartz, A. F. Keith, M. P. Boliek, and A. Zandi, “Lossless and nearly lossless compression of high-quality images,” Proc. SPIE, vol. 3025, pp. 62-70, Mar. 1997.
[13] A. Zandi, J. D. Allen, E. L. Schwartz, and M. Boliek, “CREW : Compression with reversible embedded wavelets,” in Proc. IEEE Data Compression Conf., Snowbird, UT, Mar. 1995,pp.212-221.
[14] A. R. Calderbank, I. Daubenchies, W. Sweldens, and B.-L. Yeo, ”Wavelet transforms that map integers to integers,” Appl. Comput. Harmon. Anal., vol. 5, pp. 332-369, July 1998.
[15] D. Le Gall and A. Tabatabai, ”Sub-band coding of digital images using symmetric short kemel filters and arithmetic coding technologies,” in Proc. IEEE Int, Conf. Acoustics, Speech, Signal Processing, vol. 2, New York, Apr. 1998, pp. 761-764.
[16] J. D. Villasenor, B. Beizer, and J. Liao, “Wavelet filter evaluation for image compression,” IEEE Trans. Image Processing, vol. 4, pp. 1053-1060, Aug. 1995.
[17] G, Strang and T. Nguyen, Wavelets and Filter Banks, Wellesley, MA: Wellesley-Gambridge Press, 1996.
[18] M. D. Adams and F. Kossentini, “Low-complexity reversible integer-to-integer wavelet transforms for image coding,” in Proc. IEEE Pacific Rim Conf. Communications, Computers, Signal Processing, Victoria, B.C., Canda, Aug. 1999, pp. 177-180.
[19] M. D. Adams, I, Kharitonenko, and F. Kossentini, Report on core experiment CodEff4: Performance evaluation of several reversible integer-to-integer wavelet transforms in the jpeg-2000 verification model (section 2.1), ISO/IEC JCT I/SC 29/WG I N1015 ,Oct. 1998.
[20] M. Antonono, M. barlaud, P. Mathieu, and I. Daubchies, “Image coding using wavelet transform,” IEEE Trans. Image Processing, vol. 1, no, 2, pp. 205-220, Apr. 1992.
[21] Adam, M. D. ; Kossentni, F. ”Reversible Integer-to-Integer Wavelet Transforms for Image Compression :Performance Evaluation and Analysis,” IEEE Transactions on, vol 9 Issue; 6 ,June 2000 page 1010-1024
[22] I.Daubenchies,”Orthogonal Base of Compactly supported Wavelets, ” Comn . Pure Apple. Math ,vol.41, pp.906-996,1988
[23] A. Cohen, I. Daubenchies, and J.C. Feauveau ,” Biorthogonal based of compactly Supported wavelets,”Commun Pure Appl Math,pp 485-5001992.
[24] G.Knowles, “VLSI architecture for the discrete wavelet transform” Elect-ronics Letters, vol. 26,pp.1184-1185, 1998 .
[25] K.K Parhi and T. Nishitani. “VLSI architecture for discrete wavelet transform, ” IEEE Trans. VLSI Systems, vol. 1.no.2.
pp ,191-202 ,1993.
[26] Bell-labs ，”Wim sweldens’ wavelet cascade, “
http://cm.bell-labs.com/cm/ms/who/wim/cascade
[27] Colella, D . and Heil, C., (1994) , “Characterizations of scaling functions: contnous solutions, “ SIAM J. Matrix Anal. Appl., vol. 15, no. 2, pp. 496-518
[28] 秦前清、楊宗凱，” 實用小波分析 ”，西安市，西安電子科技大學，1994
[29] 蘇由敏、黃奇，”有限區間內正交多重小波函數之建構與應用”，嘉義，中正大學，民87
[30] 李建忠, ”二維離散週期性小波轉換之高效率非分離式超大型積體電路架構,”義守大學電子工程研究所,88學年度碩士論文
[31] Unser , M .(1998) , “Shift-orthogonal wavelet bases, “ IEEE Trans. On signal processing, vol. 46, no. 7 , pp. 1827-1836
[32] Vetterli, M. amd Kovaéeviæ, J., (1995) , “Wavelets and subband coding,” press, Ohio U.S.A
[33] A. Skodras, C. Christopoulos, and T. Ebrahimi, “ The JPEG 2000 still image compression standard,” IEEE Signal Processing Magazine, pp. 36-58, Sep. 2001.
[34] W. Sweldens, “The Lifting scheme : A custom-design construction of biorthogonal wavelets,“ Appl. Comput. Harmon. Anal. pp. 188-200, 1996.
[35] Jer Min Jou, Yeu-Horng Shiau, Chin-Chi Liu, “Efficient VLSI architectures for the biorthogonal wavelet transform by filter bank and lifting scheme,” The 2001 IEEE International Symposium on , Volume: 2 , 2001, pp.529 -532 vol. 2
[36] I. Daubenchies and W. Sweldens, “Fac- toring Wavelet Transform into Lifting steps, “tech. Rep, Bell Laboratories,1996.
[37] K. C. Hung, Y. S. Hung, and Y. J. Huang, “A non-separable VLSI architecture for 2D discrete periodized wavelet transform,” IEEE Trans. on VLSI systems, vol. 9, no. 5, pp.565-576, Oct. 2001.
[38] Wei-Hsin Chang, Yew-San Lee, Wen-Shiaw Peng, Chen-Yi Lee “A line- Based, Memory Efficient and Programmable Architecture for 2D DWT using Lifting Scheme,” circuit and system, ISCAS 2001 IEEE International Symposium on, vol;4, pp. 330-333, 2001.
[39] Camille Diou, Lionel TORRES, Michel ROBERT,Gilles SASSA TELLI ,”Silicon Integration of Recursive Architecture for the 2D Wavelet Transform”.2002