隨著網路與多媒體發展日新月異，色彩量化(Color Quantization)扮演著重要的角色。在數位影像科技中，色彩量化不僅能降低影像本身資料儲存成本，還能節省網路頻寬占用量和傳輸所花費的時間。而在人造石材、紡織、噴漆和染色等工業應用上，也能透過縮減色彩種類進而達到減少調色成本的目的。因此，不論在學術還是企業領域，色彩量化技術一向都備受重視。

　　目前人造石材產業中的人造石顏色，多以人工經驗的方式調配，因此容易產生色彩失真的問題。本論文設計一種為人造石材色彩調配的色彩量化方法。在開始色彩量化前，我們先將大理石材掃描影像之像素值轉換至三維的CIE L*a*b*色彩空間，使用三階段的色彩聚合方式來量化色彩。首先是第一階段的Local-Means對色彩空間做初步量化，接著是第二階段的變異數切割(Variance-Cut)及變異數平坦化(Variance-Flatten)處理使色彩再次聚合，最後是第三階段的K-Means演算法對量化色彩進行微調。經實驗證明，我們量化的影像品質優於參考文獻的方法。

　　As the development of network and multimedia, color quantization plays an important role. In the technology of digital image, color quantization not only reduces the cost of data storage but also saves the network bandwidth and transmission time. In the industrial applications of artificial stone, textile, paint and dyeing, color quantization also reduces the cost of color allocation by decreasing the number of colors. Color quantization technique has always been highly valued for academia and industry.

　　In current industry of artificial stone, the common method to allocate stone color by human's experience usually results in the problem of color distortion. In this thesis, we propose a color quantization method for color allocation of artificial stone. Pixels of scanned stone images are transformed into CIE L*a*b* color space, and then quantized by proposed three-stage clustering algorithm. In the first stage, the method of local-means is used to initialize the quantization in color space. Then, variance-cut and variance-flatten are proposed for pixel clustering and the results are as the particle initialization of next stage. Finally, K-Means algorithm is applied for the final allocation of quantized colors. Experimental results show that our proposed method provides better image quality than that of previous work.

中文摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 ix
第一章 緒論 1
1.1 色彩量化簡介 1
1.1.1 調色盤設計與像素色彩映射 2
1.1.2 調色盤設計種類 2
1.2 色彩量化方法介紹 3
1.2.1 預聚類色彩量化方法介紹 4
1.2.2 後聚類色彩量化方法介紹 6
第二章 研究背景 9
2.1 研究動機 9
2.2 大理石材影像資料庫 9
2.3 色彩空間介紹 12
2.3.1 RGB與CIE L*a*b*色彩空間 12
2.3.2 CIE L*a*b*色差距離 17
第三章 色彩量化演算法 19
3.1 局部中心值(Local-Means)聚類方法 20
3.2 變異數切割(Variance-Cut)聚類方法 21
3.3 變異數平坦化(Variance-Flatten)方法 25
3.4 基於CIE Delta E距離公式之K-Means方法 30
第四章 實驗結果 33
4.1 實驗環境介紹 33
4.2 色彩量化參數變化比較 33
4.3 色彩量化品質比較 63
4.4 大理石材調色色彩資料庫建立 65
第五章 結論與未來展望 87
參考文獻 89

[1] N.-Y. and C.-M. Pun, “Color Image Segmentation Using Adaptive Color Quantization and Multiresolution Texture Characterization,” Journal of Signal, Image and Video Processing, vol. 8, no. 5, pp. 943-954, July 2014.
[2] W. Xia and C.-C. J. Kuo, “Color Distribution Analysis and Quantization for Image Retrieval,” in Proceedings of SPIE Conference on Storage and Retrieval for Still Image and Video Databases IV, March 1996.
[3] P. Tsaia, Y.-C. Hub, and C.-C. Chang, “A Color Image Watermarking Scheme Based on Color Quantization,” Journal of Signal Processing, vol. 84, no. 1, pp. 95-106, January 2004.
[4] C. Garcia, and G. Tziritas, “Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis,” IEEE Transactions on Multimedia, vol. 1, no. 3, pp. 95-106, September 1999.
[5] C.-K. Yang and W.-H. Tsai, “Color Image Compression Using Quantization, Thresholding, and Edge Detection Techniques All Based on The Moment-Preserving Principle,” Journal of Pattern Recognition Letters, vol. 19, no. 2, pp. 205-215, February 1998.
[6] R. S. Gentile, J. P. Allebach, and E. Walowit, “Quantization of Color Images Based on Uniform Color Spaces,” Journal of Imaging Technology, vol. 16, no. 1, pp. 11-21, February 1990.
[7] P. Heckbert, “Color Image Quantization for Frame Buffer Display,” Journal of ACM SIGGRAPH Computer Graphics, vol. 16, no. 3, pp. 297-307, July 1982.
[8] G. Joy and Z. Xiang, “Center-Cut for Color Image Quantization,” Journal of Visual Computer, vol. 10, no. 1, pp. 62-66, January 1993.
[9] M. E. Celebi, Q. Wen, and S. Hwang, “An Effective Real-Time Color Quantization Method Based on Divisive Hierarchical Clustering,” Journal of Real-Time Image Processing, vol. 10, no. 2, pp. 329-344, June 2015.
[10] M. Gervautz and W. Purgathofer, “A Simple Method for Color Quantization: Octree Quantization,” in Proceedings of CG International Conference on New Trends in Computer Graphics, 1988.
[11] Y. Hu and M.-G. Lee, “K-Means-Based Color Palette Design Scheme with the Use of Stable Flags,” Journal of Electronic Imaging, vol. 16, no. 3, pp. 033003, July 2007.
[12] M. E. Celebi, “Effective Initialization of K-Means for Color Quantization,” in Proceedings of IEEE International Conference on Image Processing, February 2010.
[13] M. E. Celebi, “Improving the Performance of K-Means for Color Quantization,” Journal of Image and Vision Computing, vol. 29, no. 4, pp.260-271, March 2011.
[14] Z. Wang, X. Sun, and D. Zhang, “A Swarm Intelligence Based Color Image Quantization Algorithm,” in Proceedings of IEEE International Conference on Bioinformatics and Biomedical Engineering, July 2007.
[15] C. Ozturk, E. Hancer, and D. Karaboga, “Color Image Quantization: A Short Review and an Application with Artificial Bee Colony Algorithm,” Journal of Informatica, vol. 25, no. 3, pp. 485-503, July 2014.
[16] X. Hu, T. Wang, and D. Li, “A New Approach of Color Quantization Based on Ant Colony Clustering Algorithm,” in Proceedings of IEEE International Conference on Information Technology, April 2005.
[17] D. Yazdani, H. Nabizadeh, E. M. Kosari, A. N. Toosi, “Color Quantization Using Modified Artificial Fish Swarm Algorithm,” in Proceedings of Australasian Joint Conference on Artificial Intelligence, 2011.
[18] Jitpakdee, P. Aimmanee, and, B. Uyyanonvara, “A Hybrid Approach for Color Image Quantization Using K-Means and Firefly Algorithms,” Journal of Computer and Information Engineering, vol. 7, no. 5, pp. 600-607, 2013.
[19] Y. -H. Chuang, “Effect of Local Governments to Levy a Special Tax on Ore: Hualien County Case Study,” Journal of the Taiwan Mining Industry, vol. 68, no. 4, pp. 1-16, December 2016.
[20] G. Sharma and H. J. Trussell, “Digital Color Imaging,” IEEE Transactions on Image Processing, vol. 6, no. 7, pp. 901-932, July 1997.
[21] P. Kay and L. Maffi, “Color Appearance and the Emergence and Evolution of Basic Color Lexicons,” Journal of American Anthropologist, vol. 101, no. 4, pp. 743-760, December 1999.
[22] S. J. Sangwine, “The Colour Image Processing Handbook,” Springer Science & Business Media, ISBN 978-1-4613-7647-7, 1998.
[23] N. A. Ibraheem, M. M. Hasan, R. Z. Khan, and P. K. Mishra, “Understanding Color Models: A Review,” ARPN Journal of Science and Technology, vol. 2, no. 3, pp. 265-275, April 2012.
[24] A. Ford and A. Roberts, “Colour Space Conversions,” Westminster University, London, pp. 1-31, 1998.
[25] C. Parraman and A. Rizzi, “Colour Coded,” Society of Dyers and Colourists, ISBN 978-0-90156-93-4, 2011.
[26] J. M. Kasson and W Plouffe, “An Analysis of Selected Computer Interchange Color Spaces,” Journal of ACM Transactions on Graphics, vol. 11, no. 4, pp. 373-405, Oct 1992.
[27] A. K. Jain, “Fundamentals of Digital Image Processing,” Prentince-Hall Inc, pp. 68-73, 1989.
[28] A. Koulaouzidis, D. K. Iakovidis, D. E. Yung, E. Rondonotti, U. Kopylov, J. N. Plevris, E. Toth, A. Eliakim, G. W. Johansson, W. Marlicz, G. Mavrogenis, A. Nemeth, H. Thorlacius, and G. E. Tontini, “KID Project: an Internet-Based Digital Video Atlas of Capsule Endoscopy for Research Purposes,” Endoscopy International Open, vol. 5, no. 6, pp. 477-483, 2017.
[29] G. Hoffmann, “CIE Color Space,” available online at http://docs-hoffmann.de/cielab03022003.pdf, accessed on Jan. 31, 2018.
[30] B. Hill, T. Roder, and F. W. Vorhagen, “Comparative Analysis of the Quantization of Color Spaces on the Basis of the CIELAB Color-Difference Formula,” Journal of ACM Transactions on Graphics, vol. 16, no. 2, pp. 109-154, April 1997.