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研究生:洪士勛
研究生(外文):Shih-Syun Hong
論文名稱:改良式碎形編碼於數位浮水印之應用
論文名稱(外文):Modified Fractal Coding for Digital Watermarking Application
指導教授:洪麟
指導教授(外文):Lin Hong
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電機工程系
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:101
畢業學年度:100
語文別:中文
論文頁數:76
中文關鍵詞:碎形影像壓縮模糊分群差分進化離散餘弦轉換數位浮水印
外文關鍵詞:Fractal image compressionFuzzy clusteringDifferential evolutionDiscrete cosine transformDigital watermarking
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碎形影像壓縮是基於本身自我相似(self-similarity)特性和切割式疊代函數系統(PIFS)來編碼,各個值域區塊利用全域搜尋法找尋最相似的定義域區塊與其收斂仿射轉換函數。然而,由於使用全域搜尋法將導致編碼時間過長,因此本文提出一個基於差分進化自動模糊分群法來加快編碼。本文方法可分兩步驟,第一階段先將定義域和值域區塊分群,每個值域區塊只對屬於同一群的定義域區塊做相似量測計算,有效地減少搜尋範圍。第二階段將第一階段相似匹配不滿意的值域區塊做四元樹切割,用更小的值域區塊再相似比對。由模擬結果可知,本篇所提的改良式碎形編碼方法能有效減少編碼時間並提高解壓縮後影像的品質。

進一步將改良式碎形編碼法應用在數位浮水印上,有別於過去文獻都是將浮水印隱藏在碎形碼的參數中,本文所用的方法是藉由修改值域區塊的均方差值來達到浮水印的嵌入,對於嵌入浮水印後的影像品質與擷取程序的計算複雜度方面都比以前的方法來得優秀。最後將本文方法與其他兩個應用在DCT、DWT頻率域之浮水印互相比較,並且透過裁剪、縮放、均值濾波、中值濾波、JPEG壓縮等破壞,來比較三者間之強韌性。實驗結果證明,不論是影像品質或是對於各種攻擊的抵抗性,本文方法都優於其他兩種方法,雖然對於各種攻擊的抵抗不一定都是最好,但整體來看有著平均較佳的強健性。
Fractal image compression is based on self-similarity property and partitioned iterated function system (PIFS) to accomplish the encoder process. Each range block explores the best-match domain block and its contractive affine transformation function by full search method. However, it is time-consuming in the encoding process for full search method, so an automatic fuzzy clustering based on differential evolution method was proposed to speed up the encoding process. There are two stages for this method, the first stage classified all domain and range blocks and then the similarity measurement of each range block was calculated for the domain blocks belonging to the same group. In this way, the search space was reduced effectively. On second stage, the range block with similarity measurement not satisfied in the first stage was segmented by quad-tree partitioning, then the similarity of smaller range blocks were calculated again. Simulation results show that the proposed modified fractal coding method can reduce the encoding time effectively as well as improve the quality of retrieved image.

Furthermore, the modified fractal coding method was used for digital watermarking application. It is different from the literatures that usually hide the watermarks in parameters of fractal code; in this paper, the mean square error of range block was modified to achieve the watermarks embedded. This method is better than other methods in watermarked image quality and computational complexity of extracting process. Finally, this technique will be compare with the other two watermarking algorithms (DCT and DWT). Under the attacks of cropping, scaling, mean filter, median filter, JPEG compression, the proposed method shows its outperformance over the others in image quality and robustness against attacks. Although the resistance for various attacks is not always the best, this method has its overall superiority among the three ones.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii

一、 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 1
1.3 內容大綱 3
二、 基本碎形影像壓縮 4
2.1 前言 4
2.2 碎形影像編碼 6
2.2.1 定義域與值域區塊切割 6
2.2.2 定義域與值域區塊相似比對 6
2.2.3 輸出編碼參數 10
2.3 碎形影像解碼 11
三、 加速碎形影像編碼 13
3.1 影像紋理特性 13
3.2 影像區塊分類 17
3.2.1 硬式分群法(Hard C-Means) 18
3.2.2 模糊分群法(Fuzzy C-Means) 20
3.2.3 有效性指標 22
3.2.4 差分進化自動模糊分群法 24
3.3 兩階段編碼 28
3.4 模擬與比較 30
3.5 討論 36
四、 數位浮水印技術 37
4.1 前言 37
4.2 空間域浮水印 38
4.3 頻率域浮水印 39
4.3.1 離散餘弦轉換浮水印 40
4.3.2 離散小波轉換浮水印 41
4.4 碎形編碼用於浮水印 44
4.4.1 位置參數浮水印 44
4.4.2 旋轉參數浮水印 46
五、 改良式碎形編碼用於浮水印 48
5.1 嵌入演算法 48
5.2 擷取演算法 49
5.3 實驗結果與數據分析 50
5.3.1 保護影像品質比較 52
5.3.2 幾何失真 54
5.3.3 訊號處理攻擊 60
5.3.4 影像壓縮破壞 67
5.3.5 討論 71
六、 結論 73

參考文獻 74
[1] M. F. Barnsley and S. Demko, “Iterated function systems and the global construction of fractals,” Proc. Roy. Soc. London A, vol. 399, pp. 243–275, 1985.
[2] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, NY, 1981.
[3] H. T. Chang and C. J. Kuo, “Iteration-free fractal image coding based on efficient domain pool design,” IEEE Trans. Image Process., vol. 9, no. 3, pp. 329–339, Mar. 2000.
[4] S. Das, A. Abraham, and A. Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, no. 1, pp. 218–237, Jan. 2008.
[5] A. E. Jacquin, “Image coding based on a fractal theory of iterated contractive image transformations,” IEEE Trans. Signal Process., vol. 1, no. 1, pp. 18–30, Jan. 1992.
[6] Y. Fisher, Fractal image compression: Theory and Application, Springer-Verlag, NY, 1994.
[7] R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic strategy for global optimization over continuous spaces,” J. Glob. Optim., vol. 11, pp. 341–359, 1997.
[8] K. T. Sun, S. J. Lee, and P. Y. Wu, “Neural network approaches to fractal image compression and decompression,” Neurocomputing, vol. 41, no 1-4, pp. 91-107, Oct. 2001.
[9] M. S. Wu, and Y. L. Lin, “Genetic algorithm with a hybrid select mechanism for fractal image compression,” Digital Signal Processing, vol. 20, no 4, pp. 1150-1161, July 2010.
[10] X. L. Xie and G. Beni, “A validity measure for fuzzy clustering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 8, pp. 841–847, Aug. 1991.
[11] C. C. Tseng, J. G. Hsieh, and J. H. Jeng, “Fractal image compression using visual-based particle swarm optimization,” Image and Vision Computing, vol. 26, no 8, pp. 1154-1162, Aug. 2008.
[12] J. H. Jeng, C. C. Tseng, and J. G. Hsieh, “Study on Huber fractal image compression,” IEEE Trans. Image Process., vol. 18, no. 5, pp. 995-1003, May 2009.
[13] M. H. Pi, C. H. Li, and H. Li, “A novel fractal image watermarking,” IEEE Trans. Multimedia, vol. 8, no. 3, pp. 488-499, June 2006.
[14] L. X. Wang, A course in fuzzy systems and control, Prentice-Hall, NJ, 1997.
[15] J. C. Bezdeck, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, no. 2-3, pp. 191-203, 1984.
[16] M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “Clustering validity checking methods: Part II,” SIGMOD Rec., vol. 31, no. 3, pp. 19-27, June 2002.
[17] K. L. Wu and M. S. Yang, “A cluster validity index for fuzzy clustering,” Pattern Recognition Letters, vol. 26, no. 9, pp. 1275-1291, July 2005.
[18] J. Puate and F. Jordan, “Using fractal compression scheme to embed a signature into an image,” Proceedings of the SPIE Photonics East’96 Symposium, pp. 108-118, 1996.
[19] H. C. Wu and C. C. Chang, “Hiding digital watermarks using fractal compression technique,” Fundamenta Informaticae, vol. 58, no. 2, pp. 189-202, Nov. 2003.
[20] G. Li, Y. Zhao, and B. Yuan, “Using the fractal code to watermark images,” 6th International Conference on Signal Processing, vol. 1, pp. 829-832, 2002.
[21] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Trans. Image Process., vol. 6, pp. 1673-1687, 1997.
[22] X. G. Xia, C. G. Boncelet and G. R. Arce, “Wavelet transform based watermark for digital images,” Optics Express, vol. 3, no. 12, pp. 497-507, Dec. 1998.
[23] R. G. van Schyndel, A. Z. Tirkel. and C.F. Osborne, “A digital watermark,” in Proc. IEEE Int. Conf. Image Processing, vol. 2, pp. 86-90, Nov. 1994.
[24] M. S. Hwang, C. C. Chang, K. F. Hwang, “A watermarking technique based on one way hash functions,” IEEE Trans. On Consumer Electronics, vol. 45, no. 2, pp. 286-294, May 1999.
[25] S. D. Liii and C. F. Chen, “A robust DCT-based watermarking for copyright protection,” IEEE Trans. On Consumer Electronics, vol. 46, no. 3, pp. 415-421, Aug. 2000.
[26] M. J. Tsai, K. Y. Yu, and Y. Z. Chen, “Joint wavelet and spatial transformation for digital watermarking,” IEEE Trans. On Consumer Electronics, vol. 46, no. 1, pp. 241-245, Feb. 2000.
[27] 王文俊,《認識Fuzzy-第三版》,全華圖書,台北,2005。
[28] 戴顯權,《資料壓縮》,旗標出版,台北,2007。
[29] 周至宏,《模糊理論與應用上課講義》,國立高雄應用科技大學電機系,2011。
[30] 繆紹綱,《數位影像處理:活用MATLAB》,全華圖書,台北,1999。
[31] 連國珍,《數位影像處理Matlab》,儒林圖書,台北,2007。
[32] 潘正祥、張真誠、林詠章,《挑戰影像處理 : 數位浮水印技術》,滄海書局,台中,2007。
[33] 陳同孝、張真誠、黃國峰,《數位影像處理技術》,旗標出版,台北,2003。
[34] 周鵬程,《遺傳演算法原理與應用:活用Matlab》,全華圖書,台北,2005。
[35] 曾緯榮, “Digital image watermarking based on fractal compression scheme,” 國立高雄第一科技大學碩士論文, 2001。
[36] 鄭嘉松, “Optimization of multi-purpose watermarking algorithm based on tabu search,” 國立高雄應用科技大學碩士論文, 2004。
[37] 蔡坤龍, “Data hiding technique based on fractal orthonormal basis”, 國立中山大學碩士論文, 2005。
[38] 李維平、江長育,搭配擾動策略之差分演化演算法,資訊科技國際研討會,台中, 2010。
[39] 黃文俊, “A novel fuzzy weighted c-means method for classification,” 國立臺中教育大學碩士論文, 2009。
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