在平面電視全球普及率節節攀升趨勢下，LCD TV已成為消費者第一指名購買的3C產品。而一般消費者在考慮購買前，除了品牌的形象考量外，針對硬體本身性能中，畫面呈現的整體品質是驅動消費者最主要的購買因素。故目前各大LCD TV控制晶片廠商中，皆有其各自處理畫質的核心技術。
    每個液晶電視區域市場中甚至是每個消費者期望電視上的色彩再現和真實色彩必定有所差異，再加上每個液晶電視因面板特性而造成色彩呈現特性不同，故喜好色調處理已成為各家液晶電視控制晶片中必備的功能。傳統的色彩調整機制中，受限於硬體的限制，無法精確的計算出色域空間的正確邊限，常在調整色調及飽和度後使像素超出色域的有效值，導致過飽和的現象，喪失了畫面原有的色彩分布細節。本論文提出一項可實現的色彩調整機制，可精確計算色域空間邊限，讓像素在調整色彩的過程中，完全不會超出色域空間，保持了畫面原有的色調分布細節。
    本論文針對液晶電視畫面色彩調整提出之可硬體實現的後處理調整方法，可使之具有更加豐富及逼近自然的畫面品質，較傳統方法比較更容易在液晶電視硬體中實現。喜好色調整方法經過FPGA的模擬驗證後，證實可實際在硬體上實現，這也是與以往所提出方法最大差異之處。未來更可將本論文提出方法應用至其他顯示裝置之色彩調整上以獲得更鮮豔及精確之畫面色彩。

As the increasing share of TV market, LCD TV becomes the most popular 3C product in the minds of consumers. Besides the brand name of LCD TV, the picture quality is a very important factor to determine the purchasing decision of customer. Consequently, every corporation of LCD TV chip provider has its own picture quality processing engine. In general, the picture quality processor includes a front-end filter, deinterlacing, and post color and contrast adjustment. The work in this thesis is to propose a novel chromatic adjusting scheme of LCD TV without over-saturation, which can be implemented in hardware IP or an embedded function in TV controller SOC.
    The color setting of every branded LCD TV is not the same due to the characteristic of LCD panel and the market location. Furthermore, every user may have his own favorite color setting. Therefore, a chromatic adjustment is necessary in the LCD TV controller. The traditional methods of chromatic adjustment suffer from the hardware cost and computing power, and hence it can’t not specify the exact boundary of color space. It usually causes over-saturation after chromatic adjustment and loses the color tone detail. In this thesis, a novel chromatic adjusting scheme without any over-saturation is proposed. By exactly calculating the boundary of color space, this scheme can generate the vivid colors and preserve more detail in high saturation area of an image frame. Unlike the traditional methods, the proposed scheme can be easily implemented in hardware IP which is suitable for integration in SOC.
In this work, a novel chromatic adjusting processing scheme for LCD TV is proposed. The chromatic adjusting scheme can make the picture quality more colorful after the favorite color adjustment by the user. The image frame will have no over-saturation and preserves the image detail after this color adjusting scheme. Comparing with traditional method, this scheme can be implemented in hardware, whish means it can be easily adopted in SOC chip.

TABLE OF CONTENTS
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Challenges 3
1.3 Overview of Thesis 5
Chapter 2 Color Appearance Models 6
2.1 What is Color 6
2.2 Color Vision 7
2.1.1 The Receptors in Human Eyes 7
2.1.2 Visual Signal Transmission 8
2.1.3 Basic Relative Attributes of Color 9
2.2 Tristimulus Values 10
2.2.1 The Basic Concept of the Tristimulus values 10
2.2.2 The Tristimulus Values 12
2.2.3 Theoretical Consequences 12
2.2.4 Chromaticity Coordinates 13
2.2.5 Spectrum Locus 14
2.3 Color Appearance Models 15
2.3.1 Common Color Spaces 17
2.3.2 Computer RGB color space 17
2.3.2 CMY Color Space 18
2.3.3 CIE XYZ and xyY Color Spaces 19
2.4 Conclusion 21
Chapter 3 Picture Quality Adjustment for LCD TV 23
3.1 Color Space Introduction 23
3.2 Color Data Path in LCD TV 27
3.3 Color Adjusting Schemes by Former Researches 28
3.3.1 Scheme Proposed by LG 29
3.3.2 Scheme Proposed by Samsung 30
3.3.3 Scheme Proposed by Ku and Wang 31
3.3 Conclusion 32
Chapter 4 Color Adjustment System without Over-saturation 33
4.1 Introduction 33
4.2 New Method to Solve Oversaturation 36
4.3 Simulation results 47
4.4 Conclusion 52
Chapter 5 System Integration and Implementation of Color Adjusting Scheme 53
5.1 Introduction 53
5.2 Implementation of Boundary Look-Up-Table 53
5.3 Saturation Mapping Scheme 61
5.3 System Integration and Implementation 65
5.4 Further Improvement of Proposed System 68
Chapter 6 Conclusion and Future work 71

LISTS OF FIGURES
Figure 2.1 Relationship between light sources, objects and the human visual system.  P7
Figure 2.2 The spectrum responsibility curve of three types of cones.  P8
Figure 2.3 Possible types of connections between retinal receptors and nerve fibers.  P9
Figure 2.4 Human cones and rods absorption spectra.  P11
Figure 2.5 CIE 1931 chromacity.  P14
Figure 2.6 RGB color space.  P17
Figure 2.7 CMY color space.  P18
Figure 2.8 XYZ color space.  P19
Figure 2.9 xyY color space.  P21
Figure 3.1 A comparison of the some color spaces.  P24
Figure 3.2 Block diagram of LCD TV controller  P43
Figure 3.3 Saturation adjusting scheme proposed by LG electronics  P29
Figure 3.4 The saturation adjusting scheme proposed by Samsung electronics  P30
Figure 3.5 The saturation adjusting scheme proposed by Ku and Wang  P31
Figure 4.1 Color cube in RGB color space. P33
Figure 4.2 Color cube in YUV color space.  P35
Figure 4.3 The hue-saturation relationship in a YUV color space P36
Figure 4.4 The problems when adjusting the saturation of the pixels.  P38
Figure 4.5 The mechanism of calculating a boundary of the cube  P39
Figure 4.6 The mechanism for obtaining the boundary  P40
Figure 4.7 The mechanism for obtaining the boundary (another condition).  P43
Figure 4.8 The systematic block diagram of our scheme for adjusting color saturation.  P47
Figure 4.9(a) Original image.   P48
Figure 4.9(b) Over-saturation image.  P49
Figure 4.9(c) Image with new method.  P49
Figure 4.10(a) Original image.  P50
Figure 4.10(b) Over-saturation image.  P51
Figure 4.10(c) Image with new method.  P51
Figure 5.1 The flow of calculating color space boundary P54
Figure 5.2 The saturation value of boundary.  P58
Figure 5.3 The relationship between a given y and y1 P59
Figure 5.4 The modified flow of boundary calculation  P61
Figure 5.5 Traditional scheme of adjusting saturation  P62
Figure 5.6 The modified saturation mapping scheme  P63
Figure 5.7 Modified saturation mapping scheme  P64
Figure 5.8 Further modified saturation mapping scheme  P64
Figure 5.9 The block diagram of chromatic adjusting system  P66
Figure 5.10 Simulation FPGA board of our method   P67
Figure 5.11 Data path of simulation board   P68
Figure 5.12 Saturation adjustment with same y.  P69
Figure 5.13 Saturation adjustment to the maximum value.  P70

LISTS OF TABLES
Table 4.1 Reference points equidistantly distributed between 0 to 2πP45
Table 4.2 Simplified reference points equidistantly distributed between 0 to 2πP45
Table 5.1 The boundary look-up table P55
Table 5.2 The modified boundary look-up table P56
Table 5.3 The further modified boundary look-up table P57

[1] M. Tkalcic, and J.F. Tasic, “Colour spaces: perceptual, historical and applicational background,” EUROCON 2003. Computer as a Tool. The IEEE Region 8, vol. 1, pp. 304 – 308, 2003.
[2] Philippe Colantoni and Al, “Color Space Transformations,” http://www.couleur.org/, 2004.
[3] Y.I. Ohta, T. Kanade, and T. Sakai, “Color information for region segmentation,” Computer Graphics and Image Processing, vol. 13, pp. 222–241, 1980.
[4] D.F. Rogers, Procedural Elements for Computer Graphics. Mc Graw Hill, 1985.
[5] R. Hall, “Illumination and Color in Computer Generated Imagery,” Springer-Verlag, 1988.
[6] K.N. Plataniotis and A. N. Venetsanopoulos, “Color Image Processing and Application,” Springer, 2000.
[7] W.G.R. Hunt, “Measuring Colour,” Second Edition, Ellis Horwood Limited, 1991.
[8] W.G.R. Hunt, “The Reproduction of Colour,” Fifth Edition, Fountain Press, 1995.
[9] W.G.R. Hunt, “CIE  Colorimetry”, Colour Management for Information Systems,   Easter School, Colour & Imaging Institute, University of Derby, 1998.
[10] M.D. Fairchild, “Color Appearance Models,” Addison Wesley, Loongman,Inc., 1998.
[11] C. Poynton, “A Guided Tour of Color Space,”, New Foundations for Video Technology (Proceedings of the SMTPE Advanced Television and Electronic Imaging Conference), pp. 167-180, 1995.
[12] C.J. Li , M.R. Luo, B. Rigg, and R.W.G. Hunt, “Chromatic adaptation transform”, CMCCAT2000, Color Res. Appl., vol. 27, pp.49 –58, 2002.
[13] N. Moroney, M.D. Fairchild, R.W.G. Hunt, C.J Li, M.R. Luo, and T. Newman, “The CIECAM02 color appearance model”, IS&T/SID 10th Color Imaging Conference, Scottsdale, pp. 23-27, 2002.
[14] US Patent, US 6,122,012
[15] US Patent, US 6,791,716
[16] I. M. Pao and M. T. Sun, “Computation reduction for discrete cosine transform,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp. 264–268, June1998.
[17] I. M. Pao, and M. T. Sun, “Modeling DCT coefficients for fast video encoding,” IEEE Trans. Circuit Syst. Video Technol., vol. 9, pp. 608–616, June1999. 
[18] X. Zhou, Z. H. Yu, and S. Y. Yu, “Method for detecting all-zero DCT coefficients ahead of discrete cosine transformation and quantization,” Electron. Lett., vol. 34, no. 19, Sept. 1998.
[19] Widipedia, http://en.wikipedia.org/wiki/
[20] K. Jack, Video Demystified, 3nd ed., Newnes Press: New York, 2001
[21] US patent, US 6,028,646
[22] C. C. Ku, T. M. Wang, “Luminance-based adaptive color saturation adjustment”, Consumer Electronics, IEEE Transactions on Volume: 51, Issue: 3, Page(s): 939 - 946 , 2005
[23]M.A. Sid-Ahmed, "Selective color adjustment using a neural network as a color classifier", Proceedings of 36th Midwest Symposium, p.1460-1463, 1993.
[24] M. A. Sid-Ahmed, "Selective color tone adjustment for advanced television systems", IEEE Transactions on Consumer Electronics, Vol. 40, No. 4, p.938-943, Nov. 1994.
[25] D. Han, “A cost effective color gamut mapping architecture for digital TV color reproduction enhancement,” IEEE Trans. Consumer Electron., vol. 51, no. 1, Feb. 2005
[26] H. Trussell, M. Vrhel, and E. Saber, “Overview of the color image processing issue,” IEEE Signal Processing Mag., vol. 22, no. 1, pp. 14-22, Jan. 2005.
[27] Y. Huang, L. Hui, and K. H. Goh, “Hue-based color saturation compensation”, IEEE Int’l Symposium on Consumer Electronics, September 1-3, 2004, Reading, U.K.
[28] K. N. Plataniotis and A. N. Venetsanopoulos, “Color Image Processing and Applications”, Springer, Heidelberg, 2000
[29] M. C. Stone, A Field, “Guide to Digital Color”, 1st ed., A K Peters, Ltd., Natick, 2003
[30] T. M. Tu, S. C. Su, H. C. Shyu, and P. S. Huang, "Efficient intensity-hue-saturation- based image fusion with saturation compensation", Opt. Eng., vol. 40, pp.720, 2001
[31] J.A. Malpica, "Hue Adjustment to IHS Pan-Sharpened IKONOS Imagery for Vegetation Enhancement", Geoscience and Remote Sensing Letters, IEEE, Vol.4 , Issue 1, pp.27-31, 2007.
[32] R. S.Gentile, E.Walowitt and J. P. Allebach, "A comparison of techniques for color gamut mismatch compensation.", Journal of Imaging Technology, vol.16, pp.176–181, 1990.
[33] H. Haneishi, K. Miyata, H. Yaguchi and Y. Miyake, "A New Method for Color Correction in Hardcopy from CRT Images." Journal of Imaging Technology, vol. 37, issue 1, pp. 30–36, 1993.
[34] P. G. Herzog and M. Miller, "Gamut Mapping Using an Analytical Color Gamut Representation.", SPIE Proceedings, vol. 3018, pp. 117–128, 1997.
[35] Heuberger K. J. and Jing Z. M. (1991). "Colour Transformations from RGB to CMY." IARAIGAI Conference Proceedings Advances in Printing Science and Technology. 
[36] T. Hoshino and R. S. Berns. "Color Gamut Mapping Techniques for Color Hard Copy Images.", SPIE Proceedings, vol. 1909, pp.152–164, 1993.
[37] R. W. G. Hunt, "Revised Colour-Appearance Model for Related and Unrelated Colours" Color Research and Application, vol. 16 Issue 3, pp. 146 - 165, 1991.
[38] H. E. J. Neugebaauer, "Die Theoretischen Grundlagen des Mehrfarbendruckes" Z.wiss. Photogr., 36, 73-89, 1937. 
[39] R. W. G. Hunt, "Measuring Colour", 2nd Ed. Ellis Horwood, England, 1992. 
[40] R. G. W. Hunt, "The Reproduction of Colour in Photography, Printing & Television", 4th Ed. Fountain Press, England, 1987. 
[41] G. Wyszecki and W. S. Stiles, "Color Science : Concepts and Methods, Quantitative Data and Formula", 2nd Ed. John Wiley & Sons, New York, 1982. 
[42] R. W. G. Hunt, "The Reproduction of Color in Photography, Printing, & Televison", Fountain press, 4th ed., 1987. 
[43] W. S. Stiles and G. Wyszecki, ”Color Science”, New York: Wiley, 1982.
[44] M. D. Fairchild and R. S Berns, "Image color-appearance specification through extension of CIELAB", Color research and application, vol.18, pp.178- 190, 1993. 
[45] Y. Nayatani, "Revision of the choma and hue scales of a nonlinear color-appearance model" , Color research and application, vol. 20, pp.143-155, 1995. 
[46] M. C. Stone and W. E. Wallace, "Gamut Mapping Computer Generated Imagery", Graphics Interface '91, Calgary; Alberta (Canada); vol. 3-7 ,pp. 32-39, 1991.
[47] B. Ruetz and S. Bruno, "Color Printing methos and Apparatus Using an Out-of-Gamut Color Table", United States Patent 5,299,291, 1994.
[48] H. Y. Jin, L. C. Jiao, F. Liu and Y. T. Qi,  "Fusion of infrared and visual images based on contrast pyramid directional filter banks using clonal selection optimizing", Opt. Eng., vol. 47, Issue 2, 2008.
[49] E. J. Lee and Y. H. Ha, “Favorite color correction for reference color”, IEEE Transactions on Consumer Electronics, vol. 44, no. 1, 1998.
[50] J. Y. You and S. I. Chien, "Saturation Enhancement of Blue Sky for Increasing Preference of Scenery Images " , IEEE Transactions on Consumer Electronics, Vol. 54, No. 2, 2008.
[51] F. Naccari, S. Battiato, A. Bruna, A. Capra, and A. Castorina, “Natural scene classification for color enhancement”, IEEE Trans. Consumer Electronics, vol. 51, issue 1, pp.234-239, 2005.
[52] J. Kuang, X. Jiang, S. Quan, and A. Chiu, “A psychophysical study on the influence factors of color preference in photographic color reproduction”,  Proc. SPIE, vol. 5668, pp. 12-19, 2005.
[53] M. Tsukada, C. Funayama, and J. Tajima, “Automatic color preference correction for color reproduction”, Proc. SPIE, vol. 4300, pp. 216-223, 2000.
[54] S. S. Agaian, B. Silver,and K. A. Panetta, "Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy", Image Processing, IEEE Transactions, vol. 16, issue 3, pp. 741 - 758, 2007.
[55] K. Q. Huanga, Q. Wang and Z. Y. Wub, "Natural color image enhancement and evaluation algorithm based on human visual system", Computer Vision and Image Understanding, vol. 103, Issue 1, pp. 52-63, 2006.