博碩士論文 etd-0902103-163028 詳細資訊


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姓名 鍾潤世(JUN-SHIH CHUNG) 電子郵件信箱 markus@ailab.ee.nsysu.edu.tw
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 碩士(Master) 畢業時期 91學年第2學期
論文名稱(中) 應用在影像壓縮上的樹狀編碼簿技術
論文名稱(英) Fast constructing tree structured vector quantization for image compression
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    摘要(中) 多媒體的資料盛行的今日,如何有效傳輸成為重要課題。除了擴大頻寬外,另一途徑就是壓縮檔案,因此不同的壓縮技術紛紛出籠。我們的工作重心放置在靜態影像的壓縮技術上,運用的方法稱為tree structure VQ(Vector quantization) ,這是屬於VQ中的一種方法,目的是提高壓縮的速度。首先,我們會利用一個以distortion為基礎的adaptive resonance theory 2(ART2)方法建構一個樹狀的編碼簿,接著我們運用較有效率的搜尋方法,利用這個編碼簿將原有的圖片取代成一連串的編號,接收到檔案的使用者,就可以利用編碼簿還有編號將原來的圖片還原。在我們的方法中,我們可以較有效率的建構一個樹狀的編碼簿,並利用這個編碼簿節省編碼所需的時間,達到較快的壓縮速率和較佳的壓縮效果。
    摘要(英) In this paper, we propose a novel approach of vector quantization using a merge-based hierarchical neural network. Vector quantization(VQ)is known as a very useful technique for lossy data compression. Recently, Neural network(NN)algorithms have been used for VQ. Vlajic and Card proposed a modified adaptive resonance theory (modified ART2)[1] which is a constructing tree structure clustering method. However, modified ART2 has disadvantages of slow construction rate and constructing many redundant levels. Therefore, we propose a more efficient approach for constructing the tree in this paper. Our method establishes only those required levels without losing the fidelity of a compressed image.
    關鍵字(中)
  • 編碼簿
  • 編碼
  • 樹狀結構向量量化
  • 關鍵字(英)
  • ART2
  • code-book
  • code-word
  • tree structure vector quantization
  • 論文目次 摘要                     i
    Abstract                   ii
    第一章簡介                  1
    1.1 VQ 的基本介紹               2
    1.2 code-book 的區別             4
    1.3 壓縮品質的評量              6
    第二章VQ 的方法介紹              9
    2.1 Enhanced LBG(ELBG)           10
    2.2 Hierarchical SOM(HSOM)         14
    2.3 Modified ART2              17
    第三章我們的方法               21
    3.1 樹狀code-book 的建立           22
    3.2 單層的學習(one level learning)     24
    3.3 編碼的方法(algorithm for encoding)   31
    第四章模擬與分析               35
    第五章總結                  54
    參考文獻                   56
    參考文獻 [1] Chang, C. and Shiue, C., “Tree Structured Vector Quantization with Dynamic Path Search,” in Proceedings of International Workshops on Parallel Processing, Sept. 1999
    [2] Russo, M. and Patane, G., “Improving the LBG Algorithm,” in Lecture Notes in Computer Science. New York: springer-Verlag, pp.621-630, 1999.
    [3] Russo, M. and Patane, G., “The enhanced LBG Algorithm,” Neural Networks, pp.1219-1237, 2001.
    [4] Vlajic, N. and Card, C., “Vector quantization of images using modified adaptive resonance algorithm for hierarchical clustering,” in IEEE Transactions on Neural Networks, Vol.12, Sept. 2001.
    [5] Gersho, A., and Gray, R. “Vector quantization and signal compression,” Boston: Kluwer, 1992.
    [6] Sayood, K., “Introduction to Data Compression,” CA: Morgan Kaufmann, 2nd ed., 2000.
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    [8] Lloyd, S., “Least squares quantization in PCMs ” , Murray Hill: Bell Telephone Laboratories paper.
    [9] Balakrishnan, M., Pearlman, W. A. and Lu, L. ,“Variable Rate Tree-structure Vector Quantization,” in IEEE Transactions on Information Theory, pp.917-930, July 1995.
    [10] Barbalho, M., Duarte, A., Neto, D., Costa, F. and Netto, A., “Hierarchical SOM applied to image compression,” in Proceedings of International Joint Conference on Neural Networks, pp.442 -447, July 2001.
    [11] Karayiannis B., Pai P. and Zervos H., “Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition,” in IEEE Transactions on Image Processing, Vol.7, Aug. 1998.
    [12] MacQueen J., “Some methods for classification and analysis of multivariate observations,” in proceedings of the fifth Berkeley symposium on Math. Stat. and prob., pp. 281-296, 1967.
    [13] Forgey E., “Cluster analysis of multivariate data: efficiency vs. inter-pretability of classification.” Biometrics, 1965.
    [14] Mukherjee D. and Mitra K., “Vector set partitioning with classified successive refinement VQ for embedded wavelet image coding,” in Proceedings of IEEE International Symposium on circuits and Systems, pp. 25-28, June 1998.
    [15] Bayazit U. and Pearlman A., “Variable length constrained storage tree structure vector quantization,” in IEEE Transactions on Image Processing, Vol.8, March 1999.
    [16]Koikkalainen P. and Oja E., “Self-organizing hierarchical feature maps,” in proceedings of International Joint Conference on Neural Networks, pp. 279 -284, June 1990.
    [17] Vlajic N., Card C. and Kunz T., “Image-compression for wireless World Wide Web browsing: a neural network approach,” in proceeding of IEEE International Joint Conference on Neural Networks, Vol. 1, July 2000.
    [18] Lee H. and Lee D., “A gain-shape vector quantizer for image coding,” in proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 141-144, April 1986.
    [19] Dong-Chul Park and Young-June Woo, “Weighted centroid neural network for edge preserving image compression” in IEEE Transactions on Neural Networks, Vol. 12, Sept. 2001.
    [20] Ho Y.-S. and Gersho A., “Variable-rate multi-stage vector quantization for image coding,” in proceedings of International Conference on Acoustics, Speech, and Signal Processing, pp.1156-1159, 1988.
    [21] Kusumoputro B., Widyanto R., Fanany I. and Budiarto H., “Improvement of artificial odor discrimination system using fuzzy-LVQ neural network,” in Proceeding of International Conference on Computational Intelligence and Multimedia Applications, pp. 474-478, Sept. 1999.
    [22] Hammer B., Brandt A. and Schielein M., “Hierarchical encoding of image sequences using multistage vector quantization,” in proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.1055 -1058, Apr. 1987.
    [23] Hung Yuen, Kam-Chi Li and Hanzo L., “Efficient variable rate vector quantization using quadtree segmentation,” in proceedings of IEEE International Symposium on Circuits and Systems, pp.1636-1639, 1995.
    口試委員
  • 謝朝和 - 召集委員
  • 吳志宏 - 委員
  • 洪宗貝 - 委員
  • 錢炳全 - 委員
  • 李錫智 - 指導教授
  • 口試日期 2003-07-15 繳交日期 2003-09-02

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