具有較大色域的色彩高飽和度顯示器可以顯示更豐富與鮮豔吸引人的畫面，是未來顯示器技術發展的方向。原色數目大於三的顯示器稱為多原色顯示器，這類顯示器可以有較傳統三原色顯示器更大的色域。顯示器技術的發展，將實現多原色顯示器。在實現這項技術前，有許多的技術待克服，本論文將研究關於多原色顯示器色彩轉換相關的課題。計畫研究適合於將現行標準顯示器的色域映對到四原色顯示器色域的方法，其中考慮高飽和度保色調色域映對、CUSP 色域映對以及傳真色域映對三種映對目的。本論文利用類神經網路完成達到色域映對目的之彩色信號轉換。

Displays with larger color gamut are able to show more colorful and attractive image, which will become the main feature of the next generation flat panel displays. Displays with more than three primaries are called multi-primary displays. The color gamut of such displays is larger than that of traditional three-primary displays. The advance of display technology will realize multi-primary displays. However, there are many technologies must be developed before such displays become popular. This thesis studies the issue of color transform for multi-primary displays, in which the color gamut mapping transforming the color space of HDTV to that of four-primary displays is considered. Lightness linear color gamut mapping, cusp color gamut mapping, and colorimetric color gamut mapping are studied and compared, in which neural networks are used for signal transformation.

第一章 緒論 1-1 引言 1-2 研究目的 第二章 色彩度量學 2-1 色彩度量學的目的 2-2 視覺及色彩度量學的發展 2-3 色彩管理 2-4-1 均勻色彩空間 2-4-2 CIELAB 及CIELUV 2-5 sRGB 色彩空間 2-6 原色轉換流程 2-7 色域對映(CGM)的基本概念 2-8 色域對映計算公式 第三章 類神經網路 3-1 什麼是類神經網路 3-2 類神經網路的組成及運作 3-3 類神經網路之優缺點 3-4 倒傳遞類神經網路 3-5 倒傳遞類神經的網路架構 3-6 倒傳遞網路學習演算法 第四章 模擬步驟與方法 4-1 色域映射基本架構的建立 4-2 以類神經網路建構色域映射模型：網路參數設定 第五章 模擬數據與分析 5-1 Colorimetric CGM, CUSP CGM, LLIN CGM 三種色域 映射方式以倒傳遞類神經網路建模之優劣分析 第六章 結論 參考文獻

[1]羅梅君,“印刷色度學”,印刷科技出版社,1991.[2]江宗錫,“利用倒傳遞類神經網路作數位相機色彩非線性演繹模式之建立”,元智大學電機與資訊工程研究所碩士論文,2000.[3]Gunter Wyszecki, W. S. Stiles, “Color Scie Concepts and Methods Quantitative Data and Formulae, 2nd Edition” ,1983. [4]Mark D. Fairchild, “ Color Appearance Models”, Addison-Wesley Publishing Company, 1998. [5]Mike Stokes, Mark D. Fairchild, Roy S. Berns,“Precision Requirements for Digital Color Reproduction”, AcM Transactions on Graphics Vol.11,No.4,406-422(1992) [6]Michael Has, Todd Newman, “Color Management: Current Practics and The Adoption of a New Standard” ,http://www.color.org/. [7]Mahy, M., L. Van Eyckdenm A. Oosterlinck, “ Evaluation of Uniform Color Spaces developed after the adoption of CIELAB and CIELUV”, Color Research & Application, Vol. 19, No. 2, 105-121(1994). [8] P. C. Hung,“Colorimetric Calibration in Electronic Image Devices Using Looking-UP-Table Model and Interpolations”,Journal of Electronic Imaging, Vol.2(I),pp.53-61,1993. [9] Henry R. Kang,“Color Technology For Electronic Image Devices”, SPIE-The International Society for Optical Engineering. [10]D. I. Specht,“Probabilistic Neural Network for Classification, Mapping, or Associative Memory”,Proceeding of the IEEE International Conference on Neural Networks, Vol,1, pp. 525-532,1988. [11] F. Rosenblatt, “ The perception: a probabilistic model for information storage and organization in the brain”, Psychological Review, Vol.65, pp. 386-408, 1958. [12]M. Minsky and S. Papert, Perceptrons, Cambridge, MA, MIT Press, 1969. [13]D. E. Rumelhart and J. L. McClelland, eds., Parallel Distributed Processing: Exploration in the Microstructure of Cognition,Vol. 1, Cambridge, MA, MIT Press, 1986. [14]葉怡成, “類神經網路模式應用與實作”,儒林圖書公司,2003. [15]葉怡成, “應用類神經網路”,儒林圖書公司,1995. [16]Fairchild M. D. (1998) Color Appearance models, Addison Wesley, Inc. [17]陳振中,“類神經網路色域映射：應用於四原色顯示器”,中華大 學電機工程學系碩士論文,2005. [18]鄭忠偉,“影像色彩資訊在影像資料庫檢索與色域對映之適用性 研究”,世新大學平面傳播科技學系碩士論文,2004. [19]葉孟秋,“TFT LCD 色偏分析與改善”,中華大學資訊工程學系 學系碩士論文,2005. [20]江哲安,“異種顯示器之間的色域對映技術”,世新大學圖文傳播 暨數位出版學系碩士論文,2006. [21]黃琮耀,“TFT-LCD 色彩校正及色彩特性演譯模式研究”, 世新 大學平面傳播科技學系碩士論文,2004. [22]陳佑霖,“色外貌模式在跨媒體彩色複雜影像之再現應用研究”, 世新大學圖文傳播暨數位出版學系碩士論文,2005. [23]張家瑋,“物理、數學、與對照表模式應用於彩色輸出設備色彩 特性演譯模式之研究”, 世新大學平面傳播科技學系碩士論 文,2005. [24]International Telecommunication Union, Parameter values for the HDTV standards for production and international programme exchange, Revision 2, ITU Recommendation ITU-R BT.709, Geneva,1995. [25]IEC report:” Color management in multimedia systems-Part 2: Color management, Part 2.1: Default RGB Color Space – sRGB”, 1998. [26]. S. Tominaga,”A neural network approach to color approach in color printers,” Proc. First Color Imaging Conference, pp. 173-177, 1993. [27]. L. Ningfang and G. Zhiyun,” A comparative study of a CRT colorimetric prediction model by neural networks and the models by conventional methods,” Color Research and Application, 24, 45-51, 1999. [28]S. Satyanarayana and S. Dalal,” Video color enhancement using neural networks,” IEEE Trans. Circuit and System for Video Technology, 6, 295-307, 1996. [29]H.-S. Lee and D. Han, “Implementation of Real Time Color Gamut Mapping Using Neural Network,” IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications, Finland, 138-141, 2005.