6. Color Transforms. Computer Engineering, Sejong University. 비선형색공간 (Non-linear color space) 균등 (uniform) 색공간을위해도입

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1 Digital Image Processing 6. Color Transforms Computer Engineering, Sejong University 비선형색공간 (Non-linear color space) 인간눈의시각특성을반영 균등 (uniform) 색공간을위해도입 인간이느끼는색의차이가색공간에서의거리차이와비례하도록함 Munsell 색을광원의색이아닌물체의표면색 (Surface color) 로표현 정확한표면색을표현하기위해서는고정된조명환경에서측정필요 HSI Color space(hsi 색공간 ) 색을 Hue( 색상 ), Saturation( 채도 ), Intensity( 명도 ) 로표현 XYZ color space 와비선형함수관계 2/39

2 Blue Green Munsell 의색입체 Color = Hue(H) + Saturation(S) + Intensity(I) White YR6/12 5R6/10 5R5/12 5R4/14 5P 10PB 5PB 10P 10B 5RP Purple Purp ple blu ue 5B 5R 10RP 10R YR Red YR Red Purple Blue 10BG 5BG Yellow Red Green 10G Yellow Green Yel llow 5G 10GY 5Y 10Y 5GY Black Hue Value 3/39 명도 (Intensity) white : 10, black : 0 사이값으로표현 4/39

3 색상 (Hue) 전체영역을인간이가장잘인지하는 5개의 principal hues (5P, 5B, 5G, 5Y, and 5R ) 로나눔 5R 10R 5YR 10YR 5P 5Y 5B 5G Hue circle in the Munsell system 5/39 채도 (Saturation) 채도가높아짐에따라서순색이 되며채도가낮을수록흰색이 섞임 시각적으로일정한차이값을 갖도록배치 명도와색상값에의해서최대 채도값이달라짐 명도 Ex. 5R,5Y and 5YR :14 5RP :12 5BG : 8 채도 a Intensity/Saturation plane 6/39 of constant hue

4 은무슨색입니까? Munsell 기호 7.5YR 7/12 7/39 RGB, HSI Color Model 8/39

5 HSI Color Model 9/39 RGB to HSI RGB 신호와 HSI 신호의관계식은 H θ = 360 θ if if B G B > G θ = cos 1 0.5[( R G ) + ( R B )] 2 [( R G) + ( R B)( G B)] θ 값은 red 축을기준으로측정 3 S = 1 [min( R, G, B)] ( R + G + B) ) 1 I = ( R + G + B) 3 RGB 값은 [0,1] 사이의값으로정규화됨 RGB 색공간과의비선형함수로 HSI 색공간이결정됨 1/ 2 10/39

6 HSI 색공간 색을인간이이해하기쉽도록표현 색상 (Hue) 성분은칼라스펙트럼을표현 녹색, 오렌지등의색 (Color) 정보표현 채도 (Saturation) ti 성분은색의순도를표현 색에흰색이섞인정도를표현 핑크는빨강에비해서흰색이많이섞임 명도 (Intensity) 성분은색의밝기를표현 계조영상의밝기성분과일치 광원이아닌표면색 (Surface color) 의표현에적합 11/39 HSI Color Example 12/39

7 Color image and its components 13/39 Color image and its components 14/39

8 균일색공간 (Uniform color space) MacAdam(1942) 의실험결과xy 좌표계의거리차이가인간이느끼는색의차이와일치하지않음 새로운좌표계의도입필요 시각적색의차이가좌표계에서거리에비례해서균등하게나타나도록변환한색공간필요 => 균일색공간 (Uniform color space) 도입 15/39 MacAdam 의타원 xy 좌표계에서시각특성상동일한색차로느끼는좌표범위를연결한결과타원의형태를나타냄 ( 왼쪽그림 ) 오른쪽그림은이를시각적으로잘보여질수있도록좌표범위를 10 배확대한그림 uniform color space => MacAdam 의타원을같은크기의원을갖도록변형 16/39

9 균일색공간 (Uniform color space) (1) CIE 1960 UCS diagram(uv 좌표계 ) u = v = 4X X + 15Y + 3Z 6Y Y X + 15Y + 3Z nm 500nm 600nm 650nm y v x MacAdam s ellipses of equally perceptible color differences. (Ellipses are 10 times their actual length) nm u CIE 1960 uv UCS diagram 17/39 균일색공간 (Uniform color space) nm 0.3 v 0.2 (2) CIE 1976 UCS diagram(u v 좌표계 ):uv 좌표계를 v 축으로 배확대 4X u = X + 15Y + 3Z 9Y nm v = 600nm X + 15Y + 3Z 500nm nm 600nm 650nm 0.4 v' nm nm u nm u' CIE 1960 uv UCS diagram CIE 1976 u v UCS diagram 18/39

10 균일색공간 (Uniform color space) (3) CIE 1964 U*V*W* diagram : uv 좌표계의 3 차원직각좌표계표현 U V W = 13W = 13W ( u u (v v 1/ 3 = 25Y n n ) ) 17 u n, v n : 기준백색의 u, v U*V* chromatic diagram at W*=50 19/39 균일색공간 (Uniform color space) (4) CIE 1976 L*u*v* diagram : U*V*W* space 의 V* scale 을 50% 확장 L u v 1/3 Y = Y n = 13L ( u u ) = 13L (v vn ) n n Y L = For Y/Y n < Y n L * v* u* Sketch of CIE 1976 L*u*v* color space 20/39 Macadam s ellipses ploted in u*v* cross section of the CIE 1976 L*u*v* uniform color space

11 균일색공간 (Uniform color space) (5) CIE 1976 L*a*b* diagram Y L = 116f 16 Y n X Y a = 500 f f Xn Yn b = 200 f Y Y Z f n Z n 1/ 3 f ( q) = q for q> f (q) = q + for q Sketch of CIE 1976 L*a*b* uniform color space with outer boundary generated by optimal stimuli with respected to CIE standard illuminant D65 and CIE 1964 supplementary standard observer 21/39 균일색공간 (Uniform color space) CIE 1976 L*a*b* diagram - 가장많이사용되는 uniform color space - 인간의시각특성을가장잘표현 - 인간이느끼는색의차이가 L*a*b* 색공간에서가장효과적으로표현 22/39

12 Munsell 계색의 CIE 색좌표계표현 L*u*v* 좌표계 xy 좌표계 23/39 L*a*b* 좌표계 Pseudo-color Image Processing 특정기준에따라서흑백영상에컬러를할당하는기법 인간으로하여금시각적인인지를강조하기위해사용 명암의구분을뚜렷하게하기위함 처리기법 : Intensity slicing Gray level to color transform 24/39

13 Pseudo-color Image Processing 명도분할 (Intensity slicing) 특정명도값이나영역에특정컬러를할당 f ( x, y) = c if f ( x, y) k V k 25/39 Intensity slicing 의예 26/39

14 Intensity slicing 의예 27/39 Pseudo-color Image Processing Gray level to color transform 특정입력화소의밝기값에서로다른컬러변환을수행 그결과를 R,,G, B 영상으로제공 각컬러별변환식, 변환방법에따라서다양한영상도출가능 28/39

15 Gray level to color transform 의예 29/39 Full-color Image Processing 컬러영상처리기법 Individual component based processing 컬러영상을각각의성분으로분해 ( 예: RGB, HSI) 각성분에대해서기존의영상처리기법적용 최종결과를다시합성하여컬러영상생성 Vector-based processing 컬러영상을벡터로처리 벡터연산을이용해직접컬러영상에대한처리수행 30/39

16 Full-color Image Processing Individual component based processing Vector-based processing 31/39 Component based processing 보색관계 (Color complements) 32/39

17 Component based processing Color correction 33/39 Component based processing Histogram Processing 34/39

18 Component based processing Color image smoothing Color image and RGB space image components 35/39 Component based processing Color image smoothing HSI space image components 36/39

19 Component based processing Image segmentation 37/39 Image segmentation Vector-based processing 38/39

20 Full-color Image Processing Individual component based processing Vector-based processing 39/39