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研究生:林子揚
研究生(外文):Tzu-yang Lin
論文名稱:硬體離散小波轉換架構之位元寬度評估
論文名稱(外文):Bit-width Tradeoffs in Hardware Discrete Wavelet Transform Architecture
指導教授:葉經緯
指導教授(外文):Ching-wei Yeh
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程所
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:2006
畢業學年度:94
語文別:中文
論文頁數:58
中文關鍵詞:提升式翻轉式離散小波轉換
外文關鍵詞:JPEG2000Flippinglifting-baseDWT
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摘要
離散小波轉換(Discrete Wavelet Transform,DWT)是一種很有效率的訊號分析技術。新一代的壓縮標準JPEG2000它的時頻分析是採用離散小波轉換為核心技術。其傳統的DWT計算是內部壓縮技術上以不同的小波基底可以分別實現失真壓縮(以Daubechies(9,7) filter bank DWT為核心)以及無失真壓縮(以Integer(5,3) filter bank DWT為核心)。
目前,在離散小波轉換硬體的實現上,經由傳統的離散小波轉換發展出提升式離散小波轉換(Lifting-based DWT)的新架構,此架構跟原架構相比能以一半的面積消耗來達到相同的效能,接著在2002年,C. Huang、P. Tseng及L. Chen提出了新的翻轉式架構(Flipping structure),將乘法器藉由易位的方式,進一步減少registers的數量,以更精簡的面積來達到相同的效果及速率。
本論文的目的在研究不同位元寬度下的離散式小波轉換的兩種架構對峰值訊雜比的影響,方法是實現翻轉式的架構,以及實現提升式架構,分別將兩種架構的乘法係數採取不同小數位元長度,去比較兩種架構不同的小數位元下的最佳峰值訊雜比。
本篇論文7位元時的實驗中翻轉式小波轉換在小數位元長度為7位元時峰值雜訊比為60.64dB,提升式離散小波轉換在長度為峰值雜訊比為62.27dB。
翻轉式架構實驗中在7位元時為最佳值,而提升式離散小波轉換峰值雜訊比則是跟著位元寬度成長而相對提高,但以面積為考量7位元時能達到一定的影像重建效果,兩種架構下小數運算位元同為7位元下,翻轉式架構整數位元比提升式架構小,影像還原效果只低1.6dB,在這種情況下採用翻轉式小波轉換效果效益較佳。
Abstract
Discrete Wavelet Transform is an efficient signal analysis technique. The newly announced image compressing technique JPEG2000 is adopting DWT as the core technology. DWT is performed by convoluting the image with designed filter banks in order decompose the digital signals into the signals in the subbands.
The hardware implementation for DWT has progressed in recent year. An important progress in DWT architecture is called Lifting-based DWT. This architecture has the same performance with only half the area of the original DWT architecture. At 2002, C. Huang, P. Tseng, and L. Chen proposed a “Flipping” structure for the Lifting-based DWT, in which the multiplication positions were changed to reduce the number of registers. With this structure, the same performance can be reached with even less area.
The aim is comparison of the Peaks Signal of Noise Ratio under the difference between the Bit-width of the Flipping structure and the Lifting-based structure. Implementing Flipping structure and Lifting-based structure is the way to compare the difference in the different floating bits. The PSNR of the Flipping in our experiment is 60.64dB under the 7 bits of the floating. The PSNR of the Lifting-based structure is 62.27dB under the 7 bits of the floating.
The optimum floating Bit-width of the Flipping structure’s PSNR is 7 Bits. The PSNR of the Lifting-based structure’s PSNR is better with the longer Bit-width. The outcome of PSNR is enough for us under the 7 Bits of two kind’s DWT structures. Under the 7 floating Bits, the total Bit-width of Flipping structure is shorter than them of the Lifting-based structure and lack 1.6dB in the PSNR. The outcome of the Flipping structure is better than the Lifting-based structure.
章節目錄
摘要 ........................I
目錄 ........................II
第一章 緒論.....................1
1.1 研究背景.....................1
1.2 研究動機與目的..................2
1.3 論文章節安排...................3
第二章 相關文獻與回顧................4
2.1 離散小波轉換...................4
2.2 提升式離散小波轉換架構..............6
2.3 提升式離散小波轉換之實現機制..........10
2.4 翻轉式離散小波轉換架構.............13
2.5 移位加法器設計的提升式離散小波轉換架構.....17
2.6 高效能與有效率記憶體管路化架構...........18
第三章 管路化移位加法器之翻轉式架構.........25
3.1 硬體架構....................25
3.2 電路架構....................32
3.3 模擬驗證....................34
第四章 實驗結果與討論................40
4.1 硬體合成....................40
4.2 實驗結果....................41
4.3 討論......................55
第五章 結論與未來展望...............56
參考文獻......................57
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[2].C. Christopoulos, A. Skodras and T. Ebrahimi, “THE JPEG2000 STILL IMAGE CODING SYSTEM: AN OVERVIEW,” IEEE Trans. on Consumer Electronics, Vol. 46, No. 4, pp. 1103-1127, November 2000
[3].Stephane Mallat, “A Wavelet Tour of Signal Processing,” Mallat, Ver. 2nd, 1994
[4].W. Sweldens,”The Lifting Scheme: A New Philosophy in Biorthogonal Wavelet Constructions,” Proc. SPIE Wavelet Applications in Signal and Image Processing III, 2569, pp. 68-79, 1995
[5].I. Daubechies and W. Sweldens, “Factoring Wavelet Transforms into Lifting Steps,”tech. Rep., Bell Laboratories, 1996.
[6].I. Daubechies and W. Sweldems, “Factoring wavelet transforms into lifting steps,” Journal Fourier Anal. Applicat., vol. 4, pp. 1998.
[7].C. Huang, P. Tseng and L. Chen, “Flipping structure: An efficient VLSI architecture for lifting-based discrete wavelet transform” in Proc. IEEE Asia-Pacifc Conf. Circuits and Systems, vol. 1, 2002, pp. 383-388
[8].Chao-Tsung Huang, Po-Chih Tseng, and Liang-Gee Chen, ”Flipping Structure: An Efficient VLSI Architecture for Lifting-Based Discrete Wavelet Transform,” IEEE Trans. Signal Process., vol. 52, NO. 4,pp 1080-1089, APRIL 2004
[9].Sandro V. Silva and Sergio Bampi, “Area and Throughput Trade-offs in the Design of Pipelined Discrete Wavelet Transform Architectures,” Conference on Proceedings of the design, Automation and Test for Europe region, 2005
[10].Keshab K. Parhi, ”VLSI Digital Signal Processing Systems,” John Wiley & Sons, 1999
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[12].陳雍岱,The Residue VLSI Architecture Design For JPEG2000 Lifting-based Forward/Backward DWT,2003,雲林科技大學
[13].戴顯權、陳政一,JPEG2000,2002,紳藍出版社
[14].Bing-Fei Wu and Chung-Fu Lin, “A High-Performance and Memory-Efficient Pipeline Architecture for the 5/3 and 9/7 Discrete Wavelet Transform of JPEG2000 Codec,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, NO 12, pp 1615 –1628, DECEMBER 2005
[15].K. A. Kotteri, S. Barua, A. E. Bell, and J. E. Carletta, “A Comparison of Hardware Implementations of the Biorthogonal 9/7 DWT: Convolution Versus Lifting,” IEEE Trans. Circuits Syst.-II EXPRESS BRIEFS, vol. 52, NO. 5, pp 256-260, MAY 2005
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