Responsive image
博碩士論文 etd-0714100-151630 詳細資訊
Title page for etd-0714100-151630
論文名稱
Title
利用殘餘影像之可變小波模式作影像壓縮之研究
Image Compression Using Wavelet Based Scalable Modeling of Residual Image
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
50
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2000-06-23
繳交日期
Date of Submission
2000-07-14
關鍵字
Keywords
殘餘影像、小波轉換、可變模式
Residual Image, Scalable Modeling, Wavelet Transform
統計
Statistics
本論文已被瀏覽 5639 次,被下載 28
The thesis/dissertation has been browsed 5639 times, has been downloaded 28 times.
中文摘要
本論文利用以立方迴旋樣條插補(CCSI)作前處理配合JPEG得到高壓縮比的改良式JPEG為基礎,於編碼時預求編碼之誤差,稱為殘像,再利用小波之可擴充性壓縮對殘像作壓縮,殘像壓縮檔當成改良式JPEG之擴充碼,傳送時可依頻寬需要傳送由125倍至50倍之影像檔,且每種壓縮率之影像品質均比最佳化之JPEG好。
Abstract
This thesis is based on the modified JPEG encoding which uses a preprocessing called as Cubic Convolution Spline Interpolation to subsample the original image into lower resolution image, the subsampled image is encoded by JPEG. The modified JPEG can get very high compression ratio, it’s quality is better than the JPEG file which has the same compressing ratio, but it still is not good enough. In this thesis we use the scalable wavelet encoding to encode the residual image, which is the difference between original and compression image. Due to the high compressing ratio and scalablity. We can attach the compressed residual image with modified JPEG compressed image to get the scalable compressed image whose compressing ratio can tun from 125 to 50 and always get better quality than optimal JPEG.
目次 Table of Contents

Contents
Abstract ……………………………………………………………………………….1

Contents ……………………………………………………………………………….2
List of Tables ………………………………………………………………….….. 3
List of Figures ……………………………………………………………….….. 4
1 Introduction ……………………………………………………………………….6
1.1 Motivation and Recent Related Research ……………………………………….6
1.2 Investigated Approach……………………………………………………………7
1.3 Related Research ………………………………………………………………8
1.4 Organization of this Thesis ………………………………………………………8
2 Technical Preliminaries …………………………………………………………...9
2.1 CCSI ……………………………………………………………………………..9
2.2 JPEG …...…………….……………………………………………………….14
2.3 Wavelet Transform ….………………………………………………………..21
2.4 Arithmetic Coding ……………………………………………………………28
3 New Scalable Image Compression Algorithm .…………………….……….. 31
3.1 Encoding Algorithm ……………………………………………………………31
3.2 Decoding Algorithm ……………………………………………………………33
3.3 Wavelet Scalable Modeling …………………………………………..………35
4 Experimental Results …………………………………………………………….37
5 Conclusions and Further Research ……………………………………………... 47

References …………..……………………………………………………………..49
參考文獻 References
References
[1] T. K. Truong, L. J. Wang, I. S. Reed and W. S. Hsieh, “Image Data Compression Using Cubic Convolution Spline Interpolation,” accepted by IEEE Transactions on Image Processing.
[2] I. Daubechies, Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1992.
[3] R. R. Coifman and M. V. Wickerhauser, “Entropy-based algorithms for best basis selection,” IEEE Transactions on Information Theory, vol. 38, pp. 713-718, Mar. 1992.
[4] A. Averbuch, D. Lazar, and M. Israeli, “Image Compression Using Wavelet Transform and Multiresolution Decomposition,” IEEE Transactions on Image Processing, Vol. 5, No. 1, January 1996.
[5] W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard, Van Nostrand Reinhold, New York, 1993.
[6] G. k. Wallace, “The JPEG Still Picture Compression Standard,” IEEE Transactions on Consumer Electronics, Vol. 38, No. 1, Feb. 1992.
[7] B.G. Sherlock; A. Nagpal; D.M. Monro, “A model for JPEG quantization,” Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on , 1994 , Page(s): 176 -179 vol.1
[8] J. Jung; M. Antonini; M. Barlaud, “Optimal JPEG decoding,” Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on Volume: 1 , 1998 , Page(s): 410 -414 vol.1
[9] D.M. Monro; B.G. Sherlock, “Optimum DCT Quantization,” Data Compression Conference, 1993. DCC '93. , 1993 , Page(s): 188 -194
[10] M. Vetterli; C. Herley, “Wavelets and filter banks: theory and design,” Signal Processing, IEEE Transactions on Volume: 40 9 , Sept. 1992 , Page(s): 2207 –2232
[11] A. Graps, “An introduction to wavelets,” IEEE Computational Science and Engineering Volume: 2 2 , Summer 1995 , Page(s): 50 –61
[12] P. Steffen; P.N. Heller; R.A. Gopinath; C.S. Burrus, “Theory of regular M-band wavelet bases,” Signal Processing, IEEE Transactions on Volume: 41 12 , Dec. 1993 , Page(s): 3497 –3511
[13] S.G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on
Volume: 11 7 , July 1989 , Page(s): 674 –693
[14] G.G. Langdon, “An Introduction to Arithmetic Coding,” IBM J. Res. Develop., Vol. 28, no. 2, pp. 135-149, Mar. 1984.
[15] I. H. Witten, R. M. Neal and J. G. Cleary, “Arithmetic Coding for Data Compression,” Communication of the ACM, Vol. 30, No. 6, June 1987.

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