1. 1 Introduction to Color Spaces Author: Chik-Yau Foo E-mail: r89922082@ms89.ntu.edu.tw Mobile phone: 0920-767-580 v030305 Presenter: Wei-Cheng Lin E-mail: r97944028@ntu.edu.twr97944028@ntu.edu.tw Mobile Phone: 0912-808-362

2. 2 The EM Spectrum 10 3 10 0 10 -3 10 -6 10 -9 10 -12 10 6 10 9 10 12 10 15 10 18 10 21 Wavelength (m) Frequency (Hz) Long-wave radio Short-wave radio TV Microwave Infrared Ultraviolet X-rays Gamma rays Cosmic rays Visible spectrum  Only a small part of the EM* spectrum is visible to us.  This part is known as the visible spectrum.  Wavelength in the region of 380 nm to 750 nm. *Electro-Magnetic

3. 3 Light and the Human Eye  When we focus on an image, light from the image enters the eye through the cornea and the pupil.  The light is focused by the lens onto the retina. Lens Pupil Cornea Iris Retina Optic nerve Fovea

4. 4 Rods and Cones  When light reaches the retina, one of two kinds of light sensitive cells are activated.  These cells, called rods and cones, translate the image into electrical signals.  The electrical signals are transmitted through the optical nerve, and to the brain, where we will perceive the image. light ConeRod Retina

5. 5 Rods: Twilight Vision  130 million rod cells per eye.  1000 times more sensitive to light than cone cells.  Most to green light (about 550-555 nm), but with a broad range of response throughout the visible spectrum.  Produces relatively blurred images, and in shades of gray.  Pure rod vision is also called twilight vision. Relative neural response of rods as a function of light wavelength. 400500600700 Wavelength (nm) 1.00 0.75 0.50 0.25 0.00 Relative response

6. 6 Cones: Color Vision  7 million cone cells per eye.  Three types of cones* (S, M, L), each "tuned" to different maximum responses at:-  S : 430 nm (blue) (2%)  M: 535 nm (green) (33%)  L : 590 nm (red) (65%)  Produces sharp, color images.  Pure cone vision is called photopic or color vision. Spectral absorption of light by the three cone types 400500600700 Wavelength (nm) 1.00 0.75 0.50 0.25 0.00 Relative absorbtion S M L *S = Short wavelength cone M = Medium wavelength cone L = Long wavelength cone

7. 7 Rod visionCone vision Photopic vs Twilight Vision  There are about 20x more rods than cones in the eyes, but rod vision is poorer than cone vision.  This is because rods are distributed all over the retina, while cones are concentrated in the fovea. Rod vision Cone vision 130 million rods 7 million cones

8. 8 Eye Color Sensitivity  Although cone response is similar for the L, M, and S cones, the number of the different types of cones vary.  L:M:S = 40:20:1  Cone responses typically overlap for any given stimulus, especially for the M-L cones.  The human eye is most sensitive to green light. Spectral absorption of light by the three cone types 400500600700 Wavelength (nm) 1.00 0.75 0.50 0.25 0.00 Relative absorbtion S M L S, M, and L cone distribution in the fovea Effective sensitivity of cones (log plot) 400500600700 Wavelength (nm) 1.00 0.1 0.01 0.001 0.0001 Relative sensitivity S M L

9. 9 Theory of Trichromatic Vision  The principle that the color you see depends on signals from the three types of cones (L, M, S).  The principle that visible color can be mapped in terms of the three colors (R, G, B) is called trichromacy.  The three numbers used to represent the different intensities of red, green, and blue needed are called tristimulus values. = Tristimulus values r gb

10. 10 Seeing Colors  The colors we perceive depends on:- Illumination source  Illumination source Object reflectance factor  Object reflectance Observer spectral sensitivity  Observer response Observer response = Tristimulus values (Viewer response) r gb x x  The product of these three factors will produce the sensation of color.

11. 11 Additive Colors  Start with Black – absence of any colors. The more colors added, the brighter it gets.  Color formation by the addition of Red, Green, and Blue, the three primary colors  Examples of additive color usage:-  Human eye  Lighting  Color monitors  Color video cameras Additive color wheel

12. 12 Subtractive Colors  Starts with a white background (usually paper).  Use Cyan, Magenta, and/or Yellow dyes to subtract from light reflected by paper, to produce all colors.  Examples of Subtractive color use:-  Color printers  Paints Subtractive color wheel

13. 13 Using Subtractive Colors on Film  Color absorbing pigments are layered on each other.  As white light passes through each layer, different wavelengths are absorbed.  The resulting color is produced by subtracting unwanted colors from white. White light Pigment layers Reflecting layer (white paper) M Y C B R G K W GreenRedBlueBlackWhite Cyan YellowMagentaCyan MagentaYellow Black

14. 14 Color Matching Experiment 1.Observer views a split screen of pure white (100% reflectance). 2.On one half, a test lamp casts a pure spectral color on the screen. 3.On the other, three lamps emitting variable amounts of red, green, and blue light are adjusted to match the color of the test light. 4.The amounts of red, green and blue light used to match the pure colors were recorded when an identical match was obtained. 5.The RGB tristimulus values for each distinct color was obtained this way. Color matching experimental setup Test Light Tristimulus values Primary Mixture

15. 15 380480580680780 Wavelength (nm) 0 9 Relative power The dashed line represents daylight reflected from sunflower, while the solid line represents the light emitted from the color monitor adjusted to match the color of the sunflower. Metamerism  Spectrally different lights that simulate cones identically appear identical.  Such colors are called color metamers.  This phenomena is called metamerism.  Almost all the colors that we see on computer monitors are metamers.

16. 16 The Mechanics of Metamerism  Under trichromacy, any color stimulus can be matched by a mixture of three primary stimuli.  Metamers are colors having the same tristimulus values R, G, and B ; they will match color stimulus C and will appear to be the same color. Wavelength (nm) 780380480580680 0 9 Relative power The two metamers look the same because they have similar tristimulus values. Wavelength (nm) 780380480580680 0 9 Relative power Wavelength (nm) 780380480580680 0 9 Relative power

17. 17 Gamut  A gamut is the range of colors that a device can render, or detect.  The larger the gamut, the more colors can be rendered or detected.  A large gamut implies a large color space. 0 0 0.20.40.60.8 0.2 0.4 0.6 0.8 x y Human vision gamut Monitor gamut Photographi c film gamut

18. 18 Color Spaces  A Color Space is a method by which colors are specified, created, and visualized.  Colors are usually specified by using three attributes, or coordinates, which represent its position within a specific color space.  These coordinates do not tell us what the color looks like, only where it is located within a particular color space.  Color models are 3D coordinate systems, and a subspace within that system, where each color is represented by a single point.

19. 19 Color Spaces  Color Spaces are often geared towards specific applications or hardware.  Several types:-  HSI (Hue, Saturation, Intensity) based  RGB (Red, Green, Blue) based  CMY(K) (Cyan, Magenta, Yellow, Black) based  CIE based  Luminance - Chrominance based CIE: International Commission on Illumination

20. 20 RGB*  One of the simplest color models. Cartesian coordinates for each color; an axis is each assigned to the three primary colors red (R), green (G), and blue (B).  Corresponds to the principles of additive colors.  Other colors are represented as an additive mix of R, G, and B.  Ideal for use in computers. *Red, Green, and Blue Black (0,0,0) Cyan (0,1,1) Green (0,1,0) Yellow (1,1,0) Red (1,0,0) Magenta (1,0,1) Blue (0,0,1) White (1,1,1) RGB Color Space

21. 21 RGB Image Data Red Channel Green Channel Full Color Image Blue Channel

22. 22 CMY(K)*  Main color model used in the printing industry. Related to RGB.  Corresponds to the principle of subtractive colors, using the three secondary colors Cyan, Magenta, and Yellow.  Theoretically, a uniform mix of cyan, magenta, and yellow produces black (center of picture). In practice, the result is usually a dirty brown-gray tone. So black is often used as a fourth color. *Cyan, Magenta, Yellow, (and blacK) Magenta Yellow Cyan Blue Red Green Black White Producing other colors from subtractive colors.

23. 23 CMY Image Data Full Color Image Cyan Image (1-R) Magenta Image (1-G)Yellow Image (1-B)

24. 24 CMY – RBG Transformation  The following matrices will perform transformations between RGB and CMY color spaces.  Note that:-  R = Red  G = Green  B = Blue  C = Cyan  M = Magenta  Y = Yellow  All values for R, G, B and C, M, Y must first be normalized.

25. 25 CMY – CMYK Transformations  The following matrices will perform transformations between CMY and CMYK color spaces.  Note that:-  C = Cyan  M = Magenta  Y = Yellow  K = blacK  All values for R, G, B and C, M, Y, K must first be normalized.

26. 26 RGB – CMYK Transformations  The following matrices perform transformations between RGB and CMYK color spaces.  Note that:-  R = Red  G = Green  B = Blue  C = Cyan  M = Magenta  Y = Yellow  All values for R, G, B and C, M, Y must first be normalized.

27. 27 RGB – Gray Scale Transformations  The luminancy component, Y, of each color is summed to create the gray scale value.  ITU-R Rec. 601-1* Gray scale: Y = 0.299R + 0.587G + 0.114B  ITU-R Rec. 709 D65 Gray scale Y = 0.2126R + 0.7152G + 0.0722B  ITU standard D65 Gray scale (Very close to Rec 709!) Y = 0.222R + 0.707G + 0.071B *601-1: Based on an old television (NTSC: National Television System Committee) standard 709 : Based on High Definition TV colorimetry (Contemporary CRT phosphors) ITU : International Telecommunication Union

28. 28 RGB and CMYK Deficiencies  RGB and CMY color models limited to brightest available primaries (R, G, and B) and secondaries (CYM).  Not intuitive. We think of light in terms of color, intensity of color, and brightness.  Colors changed by changing R, G, B ratios.  Brightness changed by changing R, G, and B, while maintaining their ratios.  Intensity changed by projecting RGB vector toward largest valued primary color (R, G, or B). 0 0 0.20.40.60.8 0.2 0.4 0.6 0.8 x y Monitor RGB gamut Photographi c film gamut 6 color CMY printer gamut Hexachrome: Cyan, Magenta, Yellow, Black, Orange, Green

29. 29 HSI / HSL / HSV*  Very similar to the way human visions see color.  Works well for natural illumination, where hue changes with brightness.  Used in machine color vision to identify the color of different objects.  Image processing applications like histogram operations, intensity transformations, and convolutions operate on only an image's intensity and are performed much easier on an image in the HSI color space. *H=Hue, S = Saturation, I (Intensity) = B (Brightness), L = Lightness, V = Value

30. 30 HSI Color Space  Hue  What we describe as the color of the object.  Hues based on RGB color space.  The hue of a color is defined by its counterclockwise angle from Red (0°); e.g. Green = 120 °, Blue = 240 °. RGB Color Space RGB cube viewed from gray-scale axis RGB cube viewed from gray-scale axis, and rotated 30° HSI Color Wheel Red 0º Green 120º Blue 240º  Saturation  Degree to which hue differs from neutral gray.  100% = Fully saturated, high contrast between other colors.  0% = Shade of gray, low contrast.  Measured radially from intensity axis. 0%0% Saturation 100%

31. 31 HSI Color Space  Intensity  Brightness of each Hue, defined by its height along the vertical axis.  Max saturation at 50% Intensity.  As Intensity increases or decreases from 50%, Saturation decreases.  Mimics the eye response in nature; As things become brighter they look more pastel until they become washed out.  Pure white at 100% Intensity. Hue and Saturation undefined.  Pure black at 0% Intensity. Hue and Saturation undefined. Hue Saturation0%100% Intensity 100% 0%

32. 32 HSI Image Data Hue Channel Saturation Channel Intensity Channel Full Image

33. 33 HSI - RGB  For a given RGB color of ( R, G, B ), the same color in the HSI Model is C(x,y) = ( H, S, I ), where  where  Saturation  Intensity  Hue

34. 34 RGB to HSI Example  Consider the RGB color defined by (215, 97,198) R = 215, G = 97, B = 198 Green (0,255,0) Red (255,0,0) Blue (0,0,255) Blue 240º Green 120º Red 0º Therefore, HSI coordinates = (308.64°, 0.843, 0.67)

35. 35 HSI to RBG  Dependent on which sector H lies in. Blue 240º Green 120º Red 0º For 120º  H  240 º For 240º  H  360 º For 0º  H  120 º

36. 36 HSV Color Space  Hue and Saturation similar to that of HSI color model.  V: Value; defined as the height along the central vertical axis.  Like Intensity in HSI, color intensity increases as Value increases. HSV: Hue, Saturation, and Value Hue Saturation0%100% Value 100% 0%

37. 37 HSV Color Space S max at V 100 Value S max at I 50 Intensity  Hue and Saturation similar to that of HSI color model.  V: Value; defined as the height along the central vertical axis.  Like Intensity in HSI, color intensity increases as Value increases.  As Value increases, hues become more saturated. Hues do not progress through the pastels to white, just as fluorescent images never change colors even though its intensity may increase. HSV is good for working with fluorescent colors. HSV: Hue, Saturation, and Value

38. 38 Intensity Operations in HSI  To change the individual color of any region in the RGB image, change the value of the corresponding region in the Hue image.  Then convert the new H image with the original S and I images to get the transformed RGB image.  Saturation and Intensity components can likewise be manipulated. Hue SaturationIntensity Original Image

39. 39 Disadvantages of HSI Color Model There are many disadvantages to the HS color model. For example:  Cannot perform addition of colors expressed in polar coordinates. Transformations are very difficult because Hue is expressed as an angle.  For color machine vision, the hues of low-saturation may be difficult to determine accurately. Systems which must be able to differentiate all colors, saturated and unsaturated, will have significant problems using the HSI representation.  When saturation is zero, hue is undefined.  Transforming between HSI and RGB is complicated.

40. 40 1931 CIE* Standard Observer (r, g, b)  The following color matching functions were obtained.  There were problems with the r, g, b color matching functions.  Negative values meant that the color had to be added to the test light before the two halves could be balanced. 380480580680780 Wavelength (nm) Tristimulus values 0.3 0.2 0.1 0.0 -0.1 0.4 r g b Color-matching functions for 1931 Standard Observer, the average of 17 color-normal observers having matched each wavelength of the equal energy spectrum with primaries of 435.8nm, 546.1 nm, and 700 nm, on a bipartite 2° field, surrounded by darkness. *Commission Internationale de L’Éclairage

41. 41 1931 CIE Standard Observer (x, y, z)  CIE adopted another set of primary stimuli, designated as X, Y, and Z.  Special properties of X, Y, Z:-  Imaginary (non-physical) primary.  All luminance information is contributed by Y.  Linearly related to R, G, B.  Non-negative values for all tristimulus values. 380480580680780 Wavelength (nm) 2.0 1.5 1.0 0.5 0.0 Tristimulus values z x y 1931 standard observer (2° observer).

42. 42 CIE 1931 xy Chromaticity Diagram  2D projection of 3D CIE XYZ color space onto X + Y + Z =1 plane.  x and y calculated as follows:-  The chromaticity of a color is determined by ( x, y ).

43. 43 CIE 1931 xy Chromaticity Diagram  For color C, where C  0.5 X + 0.4 Y + 0.1 Z  Color C is represented as (0.5, 0.4) on the Chromaticity diagram. (0.5, 0.4)

44. 44 CIE 1931 xyY Chromaticity Diagram  Each point on xy corresponds to many points in the original 3D CIE XYZ space.  Color is usually described by xyY coordinates, where Y is the luminance, or lightness component of color.  Y starts at 0 from the white spot (D65) on the xy plane, and extends perpendicularly to 100.white spot  As the Y increases, the colors become lighter, and the range of colors, or gamut, decreases.

45. 45 CIE XYZ D65 to sRGB*  The following transformations allow transformations between CIE XYZ D65 and the sRGB color models. *sRGB = Standard RGB, the standard for Internet use.

46. 46 CIE XYZ Rec. 609-1 - RGB  The following are the transformations needed to convert between CIE XYZ Rec.609-1 and RGB.

47. 47 CIE XYZ - RGB Rec. 709  Use the following matrices to transform between CIE XYZ and Rec. 709 RGB (with its D65 white).

48. 48 XYZ D65 - XYZ D50 Transformations  If the illuminant is changed from D50 to D65, the observed color will also change.  The following matrices enable transformations between XYZ D65 and XYZ D50.

49. 49 Inadequacies in the 1931 xy Chromaticity Diagram  Each line in the diagram represents a color difference of equal proportion.  The lines vary in length, sometimes greatly, depending on what part of the diagram they're in.  The differences in line length indicates the amount of distortion between parts of the diagram.

50. 50 CIE 1960 u,v Chromaticity Diagram  To correct for the deformities in the 1931 xy diagram, a number of uniform chromaticity scale (UCS) diagrams were proposed.  The following formula transforms the XYZ values or x, y coordinates to a set of u, v values, which present a visually more accurate 2D model.

51. 51 CIE 1976 u', v' Chromaticity Diagram  But the 1960 uv diagram was still unsatisfactory.  In 1975, CIE modified the u, v diagram and by supplying new ( u', v' ) values. This was done by multiplying the v values by 1.5. Thus in the new diagram u' = u and v' = 1.5 v.  The following formulas allow transformation between u’v’ and xy coordinates.

52. 52 CIE 1976 u', v' Chromaticity Diagram  Each line in the diagram represents a color difference of equal proportion.  While the representation is not perfect (it can never be), the u', v' diagram offers a much better visual uniformity than the xy diagram.

53. 53 CIE L*u*v* Color Space/ CIELUV  Replaces uniform lightness scale Y with L*, an visually linear scale.  Equations are as follows:- where u n ’ and v n ’ refer the the reference white light or light source.

54. 54 CIE L*a*b* Color Space / CIELAB  Second of two systems adopted by CIE in 1976 as models that better showed uniform color spacing in their values.  Based on the earlier (1942) color opposition system by Richard Hunter called L, a, b. color opposition system  Very important for desktop color.  Basic color model in Adobe PostScript (level 2 and level 3)  Used for color management as the device independent model of the ICC* device profiles. CIE L*a*b* color axes *International Color Consortium

55. 55 CIE L*a*b* (cont’d)  Central vertical axis : Lightness (L*), runs from 0 (black) to 100 (white).  a-a' axis: +a values indicate amounts of red, -a values indicate amounts of green.  b-b' axis, +b indicates amounts of yellow; -b values indicates amounts of blue. For both axes, zero is neutral gray.  Only values for two color axes (a*, b*) and the lightness or grayscale axis (L*) are required to specify a color.  CIELAB Color difference,  E* ab, is between two points is given by: +a+a -a-a -b-b +b+b 100 0 L* CIE L*a*b* color axes (L 1 *, a 1 *, b 1 *) (L 2 *, a 2 *, b 2 *)

56. 56 CIELAB Image Data Full Color Image L data L-a channelL-b channel

57. 57 XYZ to CIELAB  Given X n, Y n, and Z n, which are the tristimulus values for the reference white, for a point X, Y, Z:-

58. 58 CIELAB to XYZ  Reverse transformation to XYZ, given L*a*b* values. For

59. 59 CIE L*C*h* (LCh)  Often referred to simply as LCh.  Same system is the same as the CIELab color space, except that it describes the location of a color in space by use of polar coordinates rather than rectangular coordinates.  L* is a measure of the lightness of a sample, ranging from 0 (black) to 100 (white).  C* is a measure of chroma (saturation), and represents distance from the neutral axis.  h is a measure of hue and is represented as an angle ranging from 0° to 360. H (Hue) C* (Chroma)0%100% L* (Lightness) 100% 0%

60. 60 Y’U’V’ 1 (EBU 2 ) Color Space  Standard color space used for analogue television transmissions in European TVs (PAL 3 and SECAM 4 ).  Y is the luminance (or luma) or black and white component  U and V represent the color differences: U = B - Y; V = R - Y  U represents the Blue - Yellow axis; V, the Red - Green axis.  Gamma for PAL is assumed to be 2.8 1 Y = Luminance, U and V are chrominance components 2 European Broadcasting Union 3 Phase Alternation Line video standard for Europe; U = 0.492(B-Y); V = 0.877(R-Y) 4 Sequential Couleur avec Mémoire, video standard for France, the Middle East and most of Eastern Europe Red: x R = 0.630 y R = 0.340 Green: x G = 0.310 y G = 0.595 Blue: x B = 0.155 y B = 0.070 White x W = 0.312713 y W = 0.329016

61. 61 Y'UV Channels Full Color Image Y U (Blue - Yellow) V (Red - Green)

62. 62 Nonlinear Y’U’V’ Transformations  The following matrices allow transformations of nonlinear signals between Y’U’V’ and R’G’B.

63. 63 Linear Y’U’V’ Transformations  The following matrices allow transformations of linear signals between YUV RGB and XYZ.

64. 64 Y’I’Q’ 1 Color Space  Used in NTSC 2 color broadcasting in USA; compatible with black and white television, which only uses Y.  U and V defines colors clearly, but do not align with desired human perceptual sensitivities.  Y [0..1] is the luminance (or luma) component.  I [-0.523.. 0.523] represents the Orange-Blue axis.  Q [-0.596.. 0.596] represents the Purple-Green axis. 1 Y’I’Q’ = Luminance, In-phase, and Quadrature phase. 2 National Television Standards Committee video standard for North America Red: x R = 0.67 y R = 0.33 Green: x G = 0.21 y G = 0.71 Blue: x B = 0.14 y B = 0.08 White x W = 0.310063 y W = 0.316158

65. 65 YIQ Channels Full Color Image Y Channel Q (Purple - Green)I (Orange - Blue)

66. 66 Y’I’Q’ – R’G’B’  Use the following matrices to transform linear signals between Y’I’Q’ and gamma-corrected RGB values.

67. 67 YIQ - YUV  YIQ - YUV transformation is simply a color rotation of 33º.  The following matrices can be used to transform between NTSC based YIQ and PAL based YUV.

68. 68 Y’C b C r * Color Space  Y’ is luminance, C b is the chromaticity component for blue, and C r is the chromaticity component for red.  Very closely related to the YUV, it is a scaled and shifted YUV. C b = (B - Y) / 1.772 + 0.5 C r = (R - Y) / 1.402 + 0.5  Chrominance values C b and C r are [ 0..1 ].  Deals only with digital representation of R’G’B’ signals in Y’C b C r form.  Color format for JPEG 1 and MPEG 2. Independent of scanning standard and system primaries, therefore:- No chromaticity coordinates. No CIE XYZ matrices. No assumptions about white point. No assumptions about CRT gamma. 1 JPEG = Joint Photography Experts Group 2 MPEG = Motion Pictures Experts Group

69. 69 Y'C b C r - RGB [0..+1]  Use the following matrices to convert between YC b C r and RGB ranging from [0.. +1]

70. 70 ITU-R.601 YC b C r - R’G’B’ 219  ITU-R.601 defines 16 = = 235, and 16 = = 240, with 128 corresponding to 0.  These BT.601 equations are used by many video ICs to convert between digital R’G’B’ and BT.601 YC b C r data. ITU-R.601 = International Telecommunication Union – Radio communications Recommendation 601 RGB219 = A restricted color space used to match YUV standard transmission values  The R’G’B’ values produced have a nominal range of 16 - 235.

71. 71 ITU-R.601 YC b C r - R’G’B’ 0-255  If 24 bit R’G’B’ data needs to have a range of 0-255, the following equation should be used.  The R’, G’, and B’ values must be saturated at the 0 and 255 values.

72. 72 YC b C r 4:4:4  Full resolution  YC b C r 4:4:4 is in uncompressed data format.  Each pixel has all Y, C b and C r values.  Chrominance data can be subsampled without significant degradation in image quality. YC b C r 4:4:4 Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r

73. 73 YC b C r 4:4:4 Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r YC b C r 4:2:2  Obtained by a 2:1 horizontal subsampling of YC b C r 4:4:4 values.  Often used digital cameras, and many video ICs.  Restore original colors by interpolating missing C b and C r values from the values present. Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y YC b C r 4:2:2

74. 74 YC b C r 4:2:0  YC b C r 4:2:0 obtained by a 2:1 horizontal and vertical subsampling of YC b C r 4:4:4 values.  YC b C r (or, often called “YUV”) values are often subsampled to 4:2:0 before JPEG compression.  Restore original colors by interpolating missing C b and C r values from available values. YC b C r 4:4:4 Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y YY Y C b C r Y YY Y C b C r Y YY Y C b C r Y YY YC b C r 4:2:0

75. 75 YC b C r 4:4:4 Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r YCbCr 4:1:1  YC b C r 4:1:1 obtained by a 4:1 horizontal subsampling of YC b C r 4:4:4 values.  VHS* quality color. YY C b C r YY C b C r YY C b C r YY C b C r YY YY YY YY YC b C r 4:1:1 VHS: Video Home System

76. 76 YC b C r 4:2:2 - RGB 1.Convert YC b C r 4:2:2 to YC b C r 4:4:4, through interpolation. Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y Y C b C r Y YC b C r 4:2:2 Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r Y C b C r YC b C r 4:4:4 Interpolation of Cb and Cr values

77. 77 YC b C r 4:2:2 - RGB 1.Convert YC b C r 4:2:2 to YC b C r 4:4:4, through interpolation. 2.Convert YC b C r 4:4:4 to nonlinear R’G’B’.

78. 78 YC b C r 4:2:2 - RGB 1.Convert YC b C r 4:2:2 to YC b C r 4:4:4, through interpolation. 2.Convert YC b C r 4:4:4 to nonlinear R’G’B’. 3.If necessary, convert nonlinear R’G’B’ to linear RGB by removing gamma information. For (R’, G’, B’) < 21 For (R’, G’, B’)  21

79. 79 SMPTE*-C RGB Color Space  Current color standard for broadcasting in America, replacing older NTSC standard.  Reason for standard change: original set of (YIQ) primaries being slowly changed to YUV primaries.  CRT gamma assumed to be 2.2 with NTSC, 2.8 with PAL. *Society of Motion Picture and Television Engineers Red: x R = 0.630 y R = 0.340 Green: x G = 0.310 y G = 0.595 Blue: x B = 0.155 y B = 0.070 White x W = 0.312713 y W = 0.329016

80. 80 Linear SMPTE-C RGB Transformations  The following matrices allow transformations of linear signals between SMPTE-C RGB and XYZ.

81. 81 Nonlinear SMPTE-C RGB Transformation  The transformation matrices for non-linear signals are the same as that of the older YIQ (NTSC) standard.

82. 82 ITU.BT-709 in Y'CbCr  Recent standard, defined only as an interim standard for HDTV studio production.  Defined by the CCIR (now the ITU-R) in 1988, but is not yet recommended for use in broadcasting.  The primaries are the R and B from the EBU, and a G which is midway between SMPTE-C and EBU.  CRT gamma is assumed to be 2.2. Red: x R = 0.64 y R = 0.33 Green: x G = 0.30 y G = 0.60 Blue: x B = 0.15 y B = 0.06 White (D65): x W = 0.312713 y W = 0.329016 ITU: International Telecommunication Union CCIR: Comite Consultatif International des Radiocommunications

83. 83 Linear XYZ Rec.709 – RGB D65  The following matrices allow transformation between linear signals of Rec.709 XYZ values and RGB D65.

84. 84 RGB EBU – RGB 709  The following matrices allow transformation between linear Rec. 709 RGB signals and EBU* RGB signals. European Broadcasting Union

85. 85 Nonlinear Y’C b C r 709 – R’G’B’  The following matrices allow transformation between nonlinear Rec.709 Y’CbCr signals and R’G’B’.  Scaling optimized for digital video.

86. 86 SMPTE-240M Y’P b P r (HDTV*)  This one of the developments of NTSC component coding, in which the B primary and white point were changed. With this space color, all three components Y’, P b, and P r are linked to luminance.  Standard for coding High Definition TV broadcasts in the USA.  The CRT gamma law is assumed to be 2.2. *High Definition TeleVision Red: x R = 0.67 y R = 0.33 Green: x G = 0.21 y G = 0.71 Blue: x B = 0.15 y B = 0.06 White x W = 0.312713 y W = 0.329016

87. 87 RGB 240M - RGB 709  The following transforms between SMPTE* 240M (SMPTE RP 145 or Y'P b P r ) RGB to Rec. 709 RGB. *Society of Motion Picture and Television Engineers 240M = Recommended Standard for USA’s HDTV

88. 88 RGB 240M - RGB EBU  The following transforms from SMPTE 240M (SMPTE RP 145, or YP b P r ) RGB into to Rec. 709 RGB.

89. 89 Linear SMPTE-240M XYZ - RGB  The following matrices allow linear transformations between SMPTE-240M XYZ and RGB.

90. 90 Nonlinear SMPTE-240M Y’PbPr Transformations  The following matrices allow nonlinear transformations between Y’PbPr and R’G’B’.  Scaling suited for component analogue video.

91. 91 Xerox Corporation Y’E’S’ 1  Standard proposed by Xerox Corporation.  YES has three components:  Y, or luminancy,  E, or chrominancy of the red-green axis, and  S, chrominancy of the yellow-blue axis.  The following examples assume a CRT gamma of 2.2. 1 YES = Luminance, E = red-green chromaticity, S = blue-yellow chromaticity

92. 92 Y’E’S’ to XYZ D50 Transformation  If you start with non-linear Y’E’S’ values, apply a gamma correction to convert to linear YES values first:-  Next, apply the following transformation to the linear YES.

93. 93 XYZ D50 to YES Transformation  First, apply the following transformation matrix to obtain linear YES from XYZ D50.  For non-linear Y’E’S’ values, apply a gamma correction.

94. 94 YES to XYZ D65 Transformation  As before, if you start with non-linear Y’E’S’ values, apply a gamma correction to convert to linear YES values first:-  Next, apply the following transformation to the linear YES.

95. 95 XYZ D65 to YES Transformation  First, apply the following transformation matrix to obtain linear YES from XYZ D50.  If required, apply a gamma correction to obtain Y’E’S’.

96. 96 Kodak Photo CD YCC (YC 1 C 2 ) Color Space  Based on Rec. 709 and 601-1, the YCC color space has color gamut defined by the Rec. 709 primaries and a luminance - chrominance representation of color like ITU 601-1's YCbCr.  YCC provides a color gamut that is greater than that which can currently be displayed, and is therefore suitable not only for both additive and subtractive (RGB and CMY(K)) reproduction.  Extended color gamut obtainable by the PhotoCD system is achieved by allowing both positive and negative values for each primary, allowing YCC to store more colors than current display devices, such as CRT monitors and dye-sublimation printers, can produce.

97. 97 Transformations to Encode Kodak YC 1 C 2 Data  First, apply a gamma correction:  Next, transform the R’G’B’ data into YC 1 C 2 data.  Scaling is optimized for films. For R 709, G 709, B 709  0.018

98. 98 Transformations to Encode YC 1 C 2 Data (cont’d)  Finally, store the floating point values as 8-bit integers.  The unbalanced scale difference between Chroma1 and Chroma2 is designed, according to Kodak, to follow the typical distribution of colors in real scenes.

99. 99 Transforming YC 1 C 2 Data to 24-bit RGB  Kodak YCC can store more information than current display devices can cope with (it allows negative RGB values), so the transforms from YCC to RGB are not simply the inverse of RGB to YCC, they depend on the target display system. First, recover normal Luma (Y) and Chroma (C 1 and C 2 ) data. Second, if the display primaries match Rec. 709 primaries in their chromaticity, then

100. 100 YC 1 C 2 – RGB Signal Voltages  First, recover normal Luma (Y) and Chroma (C 1 and C 2 ).  Then, calculate the RGB display voltages as follows;

101. 101 PhotoYCC - YC b C r  Transform YC b C r data into PhotoYCC color space as follows:-  The image produced may not match an image that was one encoded directly in PhotoYCC color space.  Transform PhotoYCC color space into YC b C r values as follows:-  As the PhotoYCC color space is larger than the YC b C r color space, the produced image may be poorer than the original.

102. 102 sRGB specs sRGB Viewing Environment Summary Condition sRGB Display Luminance level 80 cd/m 2 Display White Point x = 0.3127, y = 0.3290 (D65) Display model offset (R, G and B)0.0 Display input/output characteristic2.2 Reference ambient illuminance level 64 lux Reference Ambient White Point x = 0.3457, y = 0.3585 (D50) Reference Veiling Glare 0.2 cd m -2 CIE chromaticities for ITU-R BT.709 reference primaries and CIE standard illuminant Red Green Blue D65 White Point x 0.64000.30000.1500 0.3127 y 0.33000.60000.0600 0.3290 z 0.03000.10000.7900 0.3583

103. 103 Glossary of Color Models CCIR: Comite Consultatif International des Radiocommunications brightness - the human sensation by which an area exhibits more or less light. lightness - the sensation of an area's brightness relative to a reference white in the scene. luma - Luminance component corrected by a gamma function and often noted Y'. chroma - the colorfulness of an area relative to the brightness of a reference white. saturation - the colorfulness of an area relative to its brightness.

104. 104 Glossary of Illuminants and Their Reference Whites Illuminant wxwx wywy A0.4880.407 B0.3480.352 C0.3100.316 D55000.3320.348 D65000.3130.329 D75000.2990.315 E0.333

105. 105 2D Color Spaces RGB Color Space HLS Color Space SMPTE Color SpaceNTSC Color Space ITU Color Space Rec.709 Color Space HSV Color Space

106. 106 References  BARCO Introduction to Color Theory, Monitor Calibration and Color Management, http://www.barco.com/display_systems/support/colorthe/colorthe.htm http://www.barco.com/display_systems/support/colorthe/colorthe.htm  R. S. Berns, Principles of Color Technology (3 rd Ed)., 2000  S. M. Boker, The Representation of Color Metrics and Mappings in Perceptual Color Space, http://kiptron.psyc.virginia.edu/steve_boker/ColorVision2/ColorVision2.h tml http://kiptron.psyc.virginia.edu/steve_boker/ColorVision2/ColorVision2.h tml  D. Bourgin, Color spaces FAQ, http://www.inforamp.net/~poynton/notes/Timo/colorspace-faq, 1996, http://www.inforamp.net/~poynton/notes/Timo/colorspace-faq  R. Buckley, Xerox Corp., G. Bretta, Hewlett-Packard Laboratories, Color Imaging on the Internet, http://www.inventoland.net/imaging/cii/nip01.pdf, 2001 http://www.inventoland.net/imaging/cii/nip01.pdf  Color Representation, http://203.162.7.85/unescocourse/computervision/comp_frm.htm http://203.162.7.85/unescocourse/computervision/comp_frm.htm

107. 107 References (cont’d)  A. Ford and A. Roberts, Color Space Conversions, www.inforamp.net/~poynton/PDFs/coloureq.pdf, 1998 www.inforamp.net/~poynton/PDFs/coloureq.pdf  Gonzales, Woods, Digital Image Processing, 2000  A. Kankaanpaa, Color Formats, www.physics.utu.fi/ett/kurssi/sfys3066/arto_tiivis.pdf, 2000. www.physics.utu.fi/ett/kurssi/sfys3066/arto_tiivis.pdf  M. Nielsen and M. Stokes, Hewlett-Packard Company, The Creation of the sRGB ICC Profile, http://www.srgb.com/c55.pdfhttp://www.srgb.com/c55.pdf  C. Poynton, Frequently Asked Questions about Color, http://www.inforamp.net/~poynton/ColorFAQ.html, 1999 http://www.inforamp.net/~poynton/ColorFAQ.html  C. Poynton, Frequently Asked Questions about Gamma, http://www.inforamp.net/~poynton/GammaFAQ.html, 1999 http://www.inforamp.net/~poynton/GammaFAQ.html  G. Starkweather, Colorspace interchange using sRGB, http://www.microsoft.com/hwdev/tech/color/sRGB.asp, 2001 http://www.microsoft.com/hwdev/tech/color/sRGB.asp

108. 108 The End - Question and Answer Session -