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研究生:林維品
研究生(外文):Wei-Pin Lin
論文名稱:在小波轉換域的影像內插及應用
論文名稱(外文):Image interpolation in Wavelet-Transform Domain and its Applications
指導教授:陳永昌陳永昌引用關係
指導教授(外文):Yung-Chang Chen
學位類別:碩士
校院名稱:國立清華大學
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:2004
畢業學年度:92
語文別:中文
論文頁數:45
中文關鍵詞:影像放大影像壓縮
外文關鍵詞:image interpolationimage coding
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近代因為數位革命,我們將許多自然界現象運用數位的描述加以處理。然而,影像因數值化造成龐大資料量,無論在儲存或是交換上都造成困擾。將影像透過各種方式予以壓縮,成為現今經常被探討的課題。另一方面,將既有的影像放大,使它盡可能的符合真實、自然,卻往往因為硬體限制,許多影像增強的作業轉而採用數位影像處理。然而,看起來似乎是不相關的兩件事情卻是有所關聯的。對於較好的影像放大技術,我們往往可以拿來作為預測編碼用,進而達到更高的影像壓縮率。
近年來,小波轉換被廣泛的運用在許多地方,因為它可以將一個複雜的訊號分析其特性,分成許多不同的子訊號。對於不同性質的子訊號,我們可以依照需要,加以區別處理。我們利用小波解析的方法,探討自然影像透過小波解析後,低頻訊號與高頻訊號的關聯。利用估測出的高頻訊號將影像加以放大,並比較其差異。進一步地,我們在運用小波解析的漸進可調式編碼法中,將其原本應編碼的高頻訊號加以代換,改為編碼透過低頻訊號預測後結果與原值之間的誤差值。透過我們預測的結果,影像可以再壓縮的更小,亦能夠不失其原本漸進可調式編碼的特性。
綜合兩分面,我們可以在傳統的編碼法上,除了在得到相同的資訊量下擁有相同的畫質之外,還可以進一步的降低我們的位元率。
Due to hardware limitation, most interpolation method has been carried out by software and becomes an important research task. Various techniques have been proposed to improve the visual quality of interpolated quality. In addition, with a good interpolation, we can facilitate predictive image coding technique.
Wavelets are being used in a number of different applications such as image enlargement and image compression. In order to benefit both applications, we propose a method to estimate high pass output from the low pass one. Using the predicted high pass output, we would be able to enlarge the image superior to other compared interpolation methods in many situations. Moreover, we use the predicted subband to wavelet image coder. It is shown that by using the proposed method, we can profit both image enlargement and its compression.
Abstract i
Table of Contents ii
List of Figures v
List of Tables vii
Abbreviations viii

Chapter 1: Introduction 1
1.1 Motivation 1
1.2 Resolution Enhancement Problems 2
1.2.1 Definition of Resolution 2
1.2.2 Limitation of Hardware 2
1.2.3 Interpolation 3
1.3 Image Compression Problems 3
1.3.1 Lossless Compression 4
1.3.2 Lossy Compression 4
1.3.3 Wavelet and Compression 5
1.4 Thesis Organization 6

Chapter 2: Review of Some Existing Resolution Enhancement Methods 7
2.1 Overview 7
2.2 Model Description 8
2.3 Pixel Replication Interpolation 9
2.4 Bilinear Interpolation 10
2.5 Spline Functions Interpolation 12
2.6 Wavelet Interpolation 13

Chapter 3: Review of Some Existing Compression Methods using Wavelet Transform 15
3.1 Overview 15
3.2 Set Partitioning in Hierarchical Trees (SPIHT) 16
3.3 Embedded Block Coding with Optimized Truncation (EBCOT) 18
3.4 Embedded Image Coding using Zero Blocks of Sub-band / Wavelet Coefficients and Context Modeling (EZBC) 20

Chapter 4: Proposed Method 23
4.1 Overview 23
4.2 An Example 24
4.3 Interpolation Algorithm 27
4.4 Enlargement 29
4.5 Coding 30

Chapter 5: Simulation Result 33
5.1 Interpolation Environments 33
5.1.1 No Filtering Decimation 33
5.1.2 2-point Filtering Decimation 33
5.1.3 5/3 Filter Decimation 34
5.1.4 Interpolation Simulation Result 34
5.2 Coding Environments 36
5.2.1 1-level Wavelet Decomposition 36
5.2.2 Multi-level Wavelet Decomposition 38
5.2.3 More Experiment on Other Sequences 41

Chapter 6: Summary and Conclusion 43

References 44
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[2] S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: A technical overview,” IEEE Signal Processing Magazine, vol. 20, no. 3, pp. 21-36, May 2003.
[3] George Wolberg and Itzik Alfy, “Monotonic Cubic Spline Interpolation,” Proc. Computer Graphics Intl. '99, Canmore, Canada, June 1999.
[4] H. F. Ates, M. T. Orchard “Image interpolation using wavelet-based contour estimation,” Proc. ICME, 2003, July, 2003.
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[8] Shih-Ta Hsiang and John W. Woods, “Embedded image coding using zeroblocks of subband/wavelet coefficients and context modeling,” Proc. of IEEE Int. Symp. on Circuits and Systems, vol. 3, pp. 662-665, Geneva, May 2000.
[9] Shih-Ta Hsiang, “Embedded image coding using zeroblocks of subband/wavelet coefficients and context modeling,” IEEE Data Compression Conference, pp. 83-92, Snow Bird, UT, Mar. 2001.
[10] D. Taubman, “High performance scalable image compression with EBCOT,” IEEE Transactions on Image Processing, Vol.97, pp. 1158-1170, Jul 2000.
[11] A. Said and W. A. Pearlman, “A new fast and efficient image codec based on set partitioning in hierarchical tree”, IEEE Trans. Circuits and Systems for Video Technology, Vol.6, No.3,jun 1996.
[12] J.M. Shapiro, “Embedded image coding using zerotrees of wavelet Coefficients,” IEEE Trans, Signal Processing, Vol. 41 ,No. 12.Dec 1993.
[13] Aaron Deever, S. S. Hemami, “What's Your Sign?: Efficient Sign Coding for Embedded Wavelet Image Coding,” Proceedings of Data Compression Conference 2000, Snowbird, Utah, March 2000.
[14] Sam-Sheng Tsai, “Motion Information Scalability for Interframe Wavelet Video Coding,” Master Degree thesis, 2003, Department of Electrical Engineering, National Chiao Tung University, June 2003.

[15] Khalid Sayood, “Introduction to Data Compression,” Morgan Kaufmann, 2000.
[16] Gilbert Strang, Truong Nguyen, “Wavelets and Filter Banks,” Wellesley-Cambridge Press, 1996.
[17] David S. Taubman, Michael W,Marcellin, “JPEG2000 Image Compression Fundamentals Standards and Practice,” KAP, 2002.
[18] C.E. Shannon, “Prediction and Entropy of Printed English,” Bell System Technical Journal, 27:379-423, 623-656, 1948.
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