四分樹分解與四分樹碼的新架構｜國立清華大學博碩士論文庫

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研究生：	葉宗倫 Tzung-ruen Yeh
論文名稱：	四分樹分解與四分樹碼的新架構 Quadtree Decomposition And A New Scheme For Quadtree Code
指導教授：	陳朝欽 Chaur-Chin Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2000
畢業學年度：	88
語文別：	中文
論文頁數：	33
中文關鍵詞：	四分樹、四分樹分解、四分樹碼
外文關鍵詞：	quadtree, quadtree decomposition, quadtree code
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人們已經發展出許多適用於低位元率應用的破壞性影像壓縮演算法。其中，四分樹分解是一個簡單技巧，它被用來在影像中代表空間上不同解析度的範圍。並分配較多位元給影像□較複雜的區域。
四分樹分解具有簡單和在低位元率時相對較佳的表現的潛力，使之成為在低位元率的影像壓縮演算法□，值得探討的一員。

在本論文中提出一個用以產生四分樹碼的新架構。在這個架構中包含了傳統的四分樹碼和一個新的四分樹碼。這個新的四分樹碼與傳統四分樹碼相對，結合起來成為新的四分樹碼架構。在本論文中也討論了有關於這個架構的用法和限制。

論文中也提到了新架構的演算法和實驗結果。實驗結果顯示新的演算法需要大約 1.8秒去壓縮一張的灰階影像，並需要約 0.3秒來解壓縮這張壓縮圖同時維持令人滿意的影像品質。

Tzung-ruen Yeh
Many lossy image compression algorithms have been developed for low bit rate applications. Among them, quadtree decomposition is a simple technique for image representation at different resolution levels that adapts spatially, allocating more bits to those complicated areas in the image. Its simplicity and potential for relatively good performance for low bit rates makes quadtree a desirable choice for a compression scheme. A new scheme for quadtree code is proposed in this thesis. This scheme combines a new developed quadtree code with regular quadtree code. This new quadtree code is complemental to the regular one. Its usage and limitation is also discussed. The algorithms and experimental results are given. Experimental results show that the new algorithm requires about 1.8 seconds to encode a 512x512 gray level image and requires about 0.3 seconds to decode it while maintains a satisfactory image fidelity.

題要
誌謝

第一章 緒論............................................1

第二章 四分樹分解......................................2

第三章 重建濾鏡的回顧..................................3

第四章 四分樹的新架構..................................4

第五章 實驗結果........................................5

第六章 結論............................................6

附  錄

Chapter 1 Introduction.................................................................................1

Chapter2 Quadtree Decomposition.............................................................3

     2.1 Quadtree Data Structure.........................................................................................3

     2.2 Quadtree and Corresponding Images.....................................................................3

     2.3 Quadtree Code........................................................................................................5

     2.4 Homogeneity Test..................................................................................................6

             2.4.1 Absolute difference test..............................................................................6

     2.5 Quadtree Decomposition Algorithm.....................................................................7

             2.5.1 The Bottom-Up Quadtree Decomposition Algorithm...............................7

             2.5.2 Near-optimal Threshold..............................................................................9

     2.6 Reconstruction Filter.............................................................................................9

Chapter 3 A Review of Reconstruction Filters.........................................10

     3.1 Strobach's   Filter........................................................................................10

     3.2 Shusterman and Feder's   Filter...................................................................11

     3.3 Knipe and Li's   Filter..................................................................................12

             3.3.1 The   Filter........................................................................................13

             3.3.2 Reconstruction Threshold.........................................................................15

Chapter 4 A New Scheme for Quadtree Code..........................................17

Chapter 5 Experimental Results................................................................23

     5.1 Environments and Strategies................................................................................23

     5.2 Experimental Results...........................................................................................25

Chapter 6 Conclusions and Discussions....................................................31

[Aiz95] K. Aizawa and T. S. Huang, “Model-based image coding: Advanced video techniques for very low bit-rate applications”, Proc. IEEE Spec. Iss. Adv. Image Video Comput., 259-271, 1995.
[Far90] P. M. Farrelle, “Recursive Block Coding for Image Data Compression”, New York: Springer-Verlag, 1990.
[Fuh88] D. Fuhrmann, “Quadtree traversal algorithms for pointer-based and depth first representations”, IEEE Trans. Pattern Anal. Machine Intell., vol. 10, 955-960, 1988.
[Gar82] I. Gargantini, “An effective way to represent quadtrees”, Commun. ACM, vol. 25, 905-910, 1982.
[Gro83] W. I. Grosky and R. Jain, “Optimal quadtrees for image segments”, IEEE Trans. Pattern Anal. Machine Intell., vol. 5, 77-83, 1983.
[Hol90] F. C. Holroyd and D. C. Mason, “Efficient linear quadtree construction algorithm”, Image Vis. Comput., 218-224, 1990.
[Hun79] G. M. Hunter and K. Steiglitz, “Operations on images using quad-trees”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 1, 145-153, 1979.
[Jon84] L. Jones and S. Iyengar, “Space and time efficient virtual quadtrees”, IEEE Trans. Pattern Anal. Machine Intell., vol. 6, 244-247, 1984.
[Kni98] J. Knipe and X. Li, “On the Reconstruction of Quadtree Data”, IEEE Trans. on Image Processing, vol. 7, 1653-1660, 1998.
[Lan85] M. Landy, Y. Cohen, and M. Pavel, “Hierarchical coding of binary images”, IEEE Trans. Pattern Anal. Machine Intell., vol. 7, 284-298, 1985.
[Sam84] H. Samet and R. E. Webber, “On encoding boundaries with quadtrees”, IEEE Trans. Pattern Anal. Machine Intell., vol. 6, 365-369, 1984.
[Sha87] C. A. Shaffer and H. Samet, “Optimal quadtree construction algorithms”, Comput. Vis., Graph. Image Process., vol. 37, 402-419, 1987.
[Sha93] J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients”, IEEE Trans. Signal Processing, vol. 41, 3445-3462, 1993
[Shu94] E. Shusterman and M. Feder, “Image Compression via Improved Quadtree Decomposition Algorithms”, IEEE Trans. on Image Processing, vol. 3, 207-215, 1994.
[Str89] P. Strobach, “Quadtree-structured linear prediction models for image sequence processing”, IEEE Trans. Pattern Anal.,, Machine Intell., vol. 11, 742-748, 1989.
[Str90] P. Strobach, “Tree-structured scene adaptive coder”, IEEE Trans. Commun., vol. 38, 477-486, 1990.
[Str91] P. Strobach, “Quadtree-structured recursive plane decomposition coding of images”, IEEE Trans. Signal Processing, vol. 39, 1380-1397, 1991.
[Sud90] P. Sudarsana, M. Manohar, and S. S. Iyengar, “Template quadtrees for representing region and line data present in binary images”, Comput. Graph. Image Process., vol. 51, 338-354, 1990.
[Tam88] M. Tamminen and H. Samet, “Efficient component labeling of images of arbitrary dimension represented by linear bintrees”, IEEE Trans. Pattern Anal. Machine Intell., vol. 10, 579-586, 1988.

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