請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41315完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均(Jian-Jiun Ding) | |
| dc.contributor.author | Tzu-Heng Henry Lee | en |
| dc.contributor.author | 李自恒 | zh_TW |
| dc.date.accessioned | 2021-06-15T00:15:42Z | - |
| dc.date.available | 2014-07-03 | |
| dc.date.copyright | 2009-07-03 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-06-13 | |
| dc.identifier.citation | REFERENCES
A. Published Conference Paper [1] J. J. Ding, J. D. Huang, T. H. Lee, and K. H. Hsu, “Improved arbitrary-shape image compression by morphological segmentation,” CVGIP 2008. B. Digital Image Compression [2] 酒井善則、吉田俊之 共著，白執善 編譯，“影像壓縮技術”，全華，2004 [3] T. Acharya amd A. K. Ray, Image Processing Principles and Applications, John Wiley & Sons, New Jersey. C. Digital Image Processing [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing Second Edition, Prentice Hall, New Jersey, 2002. [5] W. K. Pratt, Digital Image Processing. New York, NY: Wiley-Interscience, 2001. [6] S. W. Smith, The Scientist and Engineer’s Guide to Digital Signal Processing 2nd ed., California Technical Publishing, San Diego, CA, 1999, pp. 488-494. D. The JPEG Standard [7] W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard. New York: Van Nostrand Reinhold, 1993. [8] C. K. Wallace. The JPEG still picture compression standard. Communications of the ACM, 34(4):31-44, 1991. E. JPEG 2000 [9] 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. [10] N. Ahmed, T. Natarajan, and R. Rao, “Discrete cosine transform,” IEEE Transactions on Computers, vol. C-23, pp. 90–93, Jan. 1974. [11] M. Rabbani and R. Joshi, “An Overview of the JPEG2000 Still Image Compression Standard”, Signal Processing: Image Comm., Vol. 17/1, 2002. [12] S. G. Mallat, 'A theory for multiresolution signal decomposition: the wavelet representation,' Transactions on Pattern Analysis and Machine Intelligence, vol.11, no.7, pp.674-693, Jul 1989. [13] M.J. Slattery, J.L. Mitchell, The Qx-coder, IBM J. Res. Development 42 (6) (November 1998) 767–784. F. The Other Existing Compression Standards of Still Images and Their Coding Details [14] ITU-T. ISO DIS 10918-1 Digital compression and coding of continuous-tone still images (JPEG). Recommendation T.81. [15] Nasir D. Memon, Xiaolin Wu, V. Sippy, and G. Miller, “Interband coding extension of the new lossless JPEG standard,” Proc. SPIE Int. Soc. Opt. Eng., vol. 3024, no. 47, pp.47-58, January 1997. [16] M. J. Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A low complexity, context-based, lossless image compression algorithm,” in Proc. 1996 Data Compression Conference, Snowbird, UT, Mar. 1996, pp. 140–149. [17] M. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS,” IEEE Trans. Image Processing, vol. 9, no. 8, pp. 1309–1324, Aug. 2000, originally as Hewlett-Packard Laboratories Technical Report No. HPL-98-193R1, November 1998, revised October 1999. Available from http://www.hpl.hp.com/loco/. [18] F. Ono, W. Rucklidge, R. Arps, and C. Constantinescu, 'JBIG2-the ultimate bi-level image coding standard,' Image Processing, 2000. Proceedings. 2000 International Conference on , vol.1, pp.140-143 vol.1, 2000. [19] P. Howard, F. Kossentini, B. Martins, S. Forchhammer, and W. Rucklidge, 'The emerging JBIG2 standard,' Circuits and Systems for Video Technology, IEEE Transactions on , vol.8, no.7, pp.838-848, Nov 1998. [20] CompuServe Incorporated, “Graphics Interchange Format – Version 89a,” 1987-1990. http://www.w3c.org/Graphics/GIF/spec-gifS9a.txt [21] M. Nelson, “LZW data compression,” Dr. Dobb’s Journal, pp. 29-36, 86-87, Oct. 1989. [22] G. Randers-Pehrson et al., PNG (Portable Network Graphics) specification version 1.2,” PNG Development Group, July 1999. [23] P. Deutsch, DEFLATE Compressed Data Format Specification version 1.3, IETF RFC 1951, May 1996; www.ietf.org/rfc/rfc1951.txt. [24] S. Srinivasan, C. Tu, S. L. Regunathan, R. A. Rossi, Jr., G. J. Sullivan, “HD Photo: a new image coding technology for digital photography,” Applications of Digital Image Processing XXX, Proceedings of SPIE, vol. 6696, pp. 66960A, August 2007. [25] Adobe Systems. TIFF Specification, Revision 6.0. Available: http://partners.adobe.com/public/developer/en/tiff/TIFF6.pdf. [26] Apple Inc., 'Understanding PackBits,' Developer Connection, Apple Inc., Tech. Note TN1023, 1996. [27] X. Wu and N. D. Memon, “Context-based, adaptive, lossless image coding,” IEEE Trans. Commun., vol. 45, pp. 437-444, Apr. 1997. G. Shape-Adaptive Compression Algorithms [28] S. F. Chang and D. Messerschmitt, “Transform coding of arbitrarily shaped image segments,” Proc. 1st ACM Int. Conf. Multimedia Anaheim, CA, pp. 83-90, 1993. [29] P. Kauff. and K. Schüür “Shape-adaptive DCT with block-based DC separation and a DC correction”. IEEE Trans. CSVT, vol. 8, no. 3, pages 237-242, June 1998. [30] M. Gilge, T. Engelhardt, and R. Mehlan, “Coding of arbitrarily shaped image segments based on a generalized orthonormal transform,” Signal Process: Image Commun., vol. 1, pp. 153–180, Oct. 1989. [31] J. Apostolopoulos and J. Lim, “Coding arbitrarily-shaped regions,” Proc. SPIE Visual Commun. Image Process., pp. 1713-1726, May 1995. [32] R. Stasinski and J. Konrad, “A new class of fast shape-adaptive orthogonal transforms and their application to region-based image compression,” IEEE Trans. on Circuits and systems for Video Technology, vol. 9, pp. 16–34, 1999. [33] A. Kaup and T. Aach, “Coding of segmented images using shape-independent basis functions,” IEEE Trans. Image Processing, vol. 7, pp. 937–947, July 1998. [34] F. Davoine, M. Antonini, J. Chassery, and M. Barlaud, “Fractal image compression based on Delaunay triangulation and vector quantization,’’ IEEE Trans. Image Proc., vol. 5, no. 2, pp. 338-346, 1996. [35] O. J. Kwon and R. Chellappa, 'Segmentation-Based Image Compression,' Optical Engineering, vol. 32, pp. 1581-1587, Jul. 1993. [36] T. Sikora and B. Makai, “Shape-adaptive DCT for generic coding of video,” IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 59-62, Feb. 1995. [37] T. Sikora, “Low complexity shape-adaptive DCT for coding of arbitrarily shaped image segments,” Signal Processing: Image Commun., vol. 7, pp. 381-395, Nov. 1995. [38] T. Sikora, S. Bauer, and B. Makai, “Efficiency of shape-adaptive 2-D transforms for coding of arbitrarily shaped image segments,” IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 254-258, June 1995. H. Huffman Coding [39] M. Rabbani and P. W. Jones, Digital Image Compression Techniques. Bellingham, WA: SPIE Opt. Eng. Press, 1991. [40] R. W. Hamming, Coding and Information Theory Englewood Cliffs, NJ: Prentice-Hall, 1980. [41] S. Roman, Coding and information Theory. New York: Springer-Verlag, 1992. I. Two-Dimensional Orthogonal DCT Expansion in Triangular and Trapezoid Regions [42] I. E. G. Richardson, H.264 and MPEG-4 Video Compression, Chichester, Wiley, 2003. [43] A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing, 2nd ed., London, Prentice-Hall, 1999. [44] S. S. Agaian, Hadamard Matrices and Their Applications, New York, Springer-Verlag, 1985. J. MPEG-4 Overview [45] J. Ostermann, E. S. Jang, J. S. Shin, and T. Chen, “Coding the arbitrarily shaped video objects in MPEG-4,” in IEEE Int. Conf. Image Processing Santa Barbara, CA, 1997, pp. 496-499. [46] M. Van Der Schaar , D. S. Turaga and T. Stockhammer MPEG-4 Beyond Conventional Video Coding: Object Coding, Resilience and Scalability San Rafael, CA: Morgan & Claypool, 2006. [47] T. Sikora and B. Makai, “Shape-adaptive DCT for generic coding of video,” IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 59-62, Feb. 1995. [48] T. Sikora, S. Bauer, and B. Makai, “Efficiency of shape-adaptive 2-D transforms for coding of arbitrarily shaped image segments,” IEEE Trans. Circuits Syst. Video Technol., vol. 5, pp. 254-258, June 1995. [49] I. Richardson, H.264 and MPEG-4 video compression: video coding for next generation: John Wiley and Sons, 2003. [50] 吳炳飛等著, MPEG-4視訊壓縮技術, 全華科技,. 2006年11月 K. Others [51] Z. Wang and A. C. Bovik, “Blind measurement of blocking artifacts in images,” in Proc. IEEE Int. Conf. Image Processing Vancouver, BC, Canada, Oct. 2000, pp. 981-984. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41315 | - |
| dc.description.abstract | 在近幾年的多媒體應用中，不規則形狀的影像壓縮已經變的越來越熱門。形狀自適應編碼的優點在於這種方法可以運用同一個不規則區塊中色彩強度的高相關性來對不規則形狀內的影像資訊做更好的壓縮，以達到更高的壓縮率。
相較於傳統基於方型區塊的影像壓縮，形狀自適應的影像編碼可產生相當少的區塊效應及變形失真的情形。這是由於基於方型區塊的影像壓縮忽略了影像的內容與特徵。 因為傳統的不規則形狀的影像壓縮大多依賴格拉姆-施密特正交化演算法來取得每一個不規則形狀區塊的正交化基底，它的運算複雜度是相當龐大的。因此，我們在這邊論文裡提出了一個創新的概念 – 以三角形和梯形分割為基礎的二維正交離散餘弦轉換，其效能較傳統的不規則形狀的離散餘弦轉換來說，在運算複雜度上較為節省。由於一個不規則形狀的區域嚴格來說是一個多邊形，而一個多邊形可以被分解成很多三角形及梯形，本篇論文提出的方法是相當適用在對不規則形狀的區域做轉換。 實驗結果顯示本篇論文提出的方法跟運用格拉姆-施密特演算法的方法都有一樣好的將能量集中在低頻的能力，而本篇論文提出的方法可以顯著的減少運算時間。另外，我們也可以運用本篇論文提出的方法產生可適於三角形和梯形的正交離散傅立葉基底、KLT基底、Legendre基底、Hadamard (Walsh)基底、及其他多項式基底。 | zh_TW |
| dc.description.abstract | Coding of arbitrarily shaped image region is becoming more and more popular in today’s multimedia applications. The advantage of shape-adaptive coding is that it can employ the information of arbitrarily-shaped region to exploit the high correlation of the color values within the same image segment in order to achieve a superior compression rate. Compared to the conventional block-based image coding, shape-adaptive image coding produces significantly less blocking artifacts and distortions in other forms which typically emerges in block-based image coding since its negligence of the image content and characteristics.
Because early shape-adaptive image coding relies on the Gram-Schmidt process to obtain orthogonal basis for each arbitrary region, its computational complexity could be enormous. Therefore, in this thesis, we present the two-dimensional orthogonal DCT expansion in triangular and trapezoid regions which is much more economical in terms of the complexity compared to the conventional shape-adaptive transforms. Since an arbitrarily shaped region literally is a polygon and a polygon can be decomposed into several triangular or trapezoidal regions, the proposed method is highly suitable for transforming arbitrarily shaped segments. Results show that the proposed method has the energy compact ability that is as good as the results of the Gram-Schmidt method, and significantly fast computation time. In addition, the proposed method can also be used for generating the 2-D complete and orthogonal DFT basis, KLT basis, Legendre basis, Hadamard (Walsh) basis, and polynomial basis in the trapezoid and triangular regions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T00:15:42Z (GMT). No. of bitstreams: 1 ntu-98-R96942133-1.pdf: 1943398 bytes, checksum: 1856ef01c5747cd951756f97f569b11f (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | CONTENTS
口試委員會審定書 # 誌謝 i ACKNOWLEGEMENTS iii 中文摘要 v ABSTRACT vii CONTENTS ix LIST OF FIGURES xv LIST OF TABLES xxi Chapter 1 Introduction 1 Chapter 2 Basic Still Image Compression Algorithm 5 2.1 Basic Image Compression Model 6 2.2 Transform Coding 7 2.2.1 Image Encoding Algorithm Using the Orthogonal Transform 7 2.2.2 Karhunen-Loeve Transform (KLT) 10 2.2.3 Discrete Cosine Transform (DCT) 15 2.3 The JPEG Still Picture Compression Standard 17 2.3.1 Processing Steps for DCT-Based JPEG Coding Modes 18 2.3.2 Predictive Lossless JPEG 22 Chapter 3 A Review of the Other Existing Compression Standards of Still Images 27 3.1 JPEG 2000 27 3.1.1 Fundamental Building Blocks 28 3.1.2 Wavelet Transform 30 3.2 JPEG-LS 33 3.2.1 Decorrelation/Prediction 33 3.2.2 Context Modeling 34 3.2.3 Coding Corrected Prediction Residuals 36 3.2.4 Run Length Coding in Uniform Areas 36 3.3 JBIG2 37 3.3.1 Text Image Data 38 3.3.2 Halftones 40 3.3.3 Arithmetic Entropy Coding 41 3.4 GIF 41 3.4.1 LZW Data Compression 41 3.4.2 Implementation Challenges of LZW Algorithm 45 3.4.3 Application of LZW Algorithm in Image Compression 47 3.5 PNG 48 3.5.1 Use of Huffman Coding in the Deflate Format 49 3.5.2 LZ77-Related Compression Algorithm Details 50 3.6 HD Photo (JPEG XR) 51 3.6.1 Data Hierarchy 52 3.6.2 The HD Photo Compression Algorithm 55 3.7 TIFF 6.0 55 3.7.1 Difference Predictor 55 3.7.2 PackBits Compression 56 3.7.3 Modified Huffman Compression 57 Chapter 4 Past Research on Shape-Adaptive Image Compression 59 4.1 Shape-Adaptive Orthogonal Transforms 60 4.2 Coding Using Shape-Independent Basis Functions 61 4.2.1 Discrete Linear Approximation 61 4.2.2 Orthogonal Basis Functions 62 4.2.3 Successive Approximation Using Shape-Independent Basis Functions 63 4.3 Adaptive Image Partitioning Compression Using Delaunay Triangulation 67 4.3.1 Different Techniques for Adaptive Image Partitioning 68 4.4 Segmentation-Based Image Compression 70 Chapter 5 Shape-Adaptive Image Compression 73 5.1 Morphological Segmentation Using Erosion 76 5.1.1 Modifications to Morphological Segmentation Algorithm 79 5.2 Shape-Adaptive Transform Algorithm 81 5.2.1 A Traditional Approach to Transform Arbitrarily-Shaped Image Segments 81 5.2.2 Transform Using Arbitrarily-Shape DCT Bases 81 5.2.3 Orthogonalization of the Shape-Projected Bases 85 5.3 Quantization of the Arbitrary-Shape DCT Coefficients 89 5.3.1 Adaptive Quantization of the Arbitrary-Shape DCT Coefficients 91 5.4 Coding Technique of the Image Segment 92 5.5 Simulation Results and Performance Comparison 94 5.5.1 Arbitrarily-Shaped Image Compression 94 5.5.2 Arbitrarily-Shaped Image Compression with the Erosion Operation 95 5.5.3 Performance Comparison to JPEG Standard 96 5.5.4 Arbitrarily-Shaped Image Compression with Both the Erosion Operation and Adaptive Quantization 97 5.5.5 Complexity Issues and Proposed Solution 103 5.6 Summary 104 Chapter 6 Huffman Coding 105 6.1 Instantaneous Codes 105 6.2 Huffman Codes 105 6.3 Inaccuracy in the Estimates of the Huffman Coding Probabilities 109 Chapter 7 Overview of the MPEG-4 Standard 111 7.1 Coding of Objects with Arbitrary Shapes 111 7.1.1 Shape Coding 113 7.1.2 Texture Coding 119 7.1.3 Sprite Coding 126 7.1.4 Shape Extraction and Segmentation 129 7.2 Coding of Objects with Arbitrary Shapes 130 7.2.1 Spatial Scalability 131 7.2.2 Temporal Scalability 132 7.2.3 Object-Based Scalability 132 7.2.4 Fine Granular Scalability 132 Chapter 8 Two-Dimensional Orthogonal DCT Expansion in Triangular and Trapezoid Regions 137 8.1 Complete and Orthogonal DCT Basis in the Trapezoid Region 139 8.2 Extending to Generalized Trapezoid, Triangular, and Polygonal Regions 145 8.3 Extending to Other Symmetric Orthogonal Basis 146 8.4 Applications in Image Compression and Signal Processing 148 8.5 Summary 151 Chapter 9 Geometric Progression Coding Theory 153 Chapter 10 Conclusions and Future Work 159 10.1 Conclusions 159 10.2 Future Work 160 REFERENCES 163 | |
| dc.language.iso | en | |
| dc.subject | 不規則形狀的影像壓縮 | zh_TW |
| dc.subject | 影像壓縮 | zh_TW |
| dc.subject | shape-adaptive transform | en |
| dc.subject | Image compression | en |
| dc.title | 以三角形和梯形分割為基礎的影像壓縮改良技術 | zh_TW |
| dc.title | Advanced Image Compression Techniques by Triangular and Trapezoidal Segmentations | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曾易聰(Yi-Chong Zeng),郭景明 | |
| dc.subject.keyword | 影像壓縮,不規則形狀的影像壓縮, | zh_TW |
| dc.subject.keyword | Image compression,shape-adaptive transform, | en |
| dc.relation.page | 170 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-06-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-98-1.pdf 未授權公開取用 | 1.9 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。