1. Light and Color Jehee Lee Seoul National University With a lot of slides stolen from Alexei Efros, Stephen Palmer, Fredo Durand and others

2. The Eye The human eye is a camera! –Iris - colored annulus with radial muscles –Pupil - the hole (aperture) whose size is controlled by the iris –What’s the “film”? photoreceptor cells (rods and cones) in the retina

3. The Retina

4. Retina up-close Light

5. © Stephen E. Palmer, 2002 Cones cone-shaped less sensitive operate in high light color vision Two types of light-sensitive receptors Rods rod-shaped highly sensitive operate at night gray-scale vision

6. Rod / Cone sensitivity The famous sock-matching problem…

7. © Stephen E. Palmer, 2002 Distribution of Rods and Cones Night Sky: why are there more stars off-center?

8. Electromagnetic Spectrum http://www.yorku.ca/eye/photopik.htm Human Luminance Sensitivity Function

9. Why do we see light of these wavelengths? © Stephen E. Palmer, 2002 …because that’s where the Sun radiates EM energy Visible Light

10. The Physics of Light Any patch of light can be completely described physically by its spectrum: the number of photons (per time unit) at each wavelength 400 - 700 nm. © Stephen E. Palmer, 2002

11. The Physics of Light Some examples of the spectra of light sources © Stephen E. Palmer, 2002

12. Radiometry

13. Radiant exitance Irradiance

14. Radiometry Radiance Radiant intensity

15. Photometry

16. Horn, 1986 Radiometry for color Spectral radiance: power in a specified direction, per unit area, per unit solid angle, per unit wavelength Spectral irradiance: incident power per unit area, per unit wavelength

17. Simplified rendering models: reflectance Often are more interested in relative spectral composition than in overall intensity, so the spectral BRDF computation simplifies a wavelength-by-wavelength multiplication of relative energies..* = Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

18. .* = Simplified rendering models: transmittance Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

19. The Physics of Light Some examples of the reflectance spectra of surfaces Wavelength (nm) % Photons Reflected Red 400 700 Yellow 400 700 Blue 400 700 Purple 400 700 © Stephen E. Palmer, 2002

20. The Psychophysical Correspondence There is no simple functional description for the perceived color of all lights under all viewing conditions, but …... A helpful constraint: Consider only physical spectra with normal distributions area mean variance © Stephen E. Palmer, 2002

21. The Psychophysical Correspondence MeanHue # Photons Wavelength © Stephen E. Palmer, 2002

22. The Psychophysical Correspondence VarianceSaturation Wavelength # Photons © Stephen E. Palmer, 2002

23. The Psychophysical Correspondence AreaBrightness # Photons Wavelength © Stephen E. Palmer, 2002

24. Three kinds of cones: Physiology of Color Vision

25. More Spectra metamers

26. Metameric Whites

27. Metameric lights Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

28. Color Matching

29. Color matching experiment 1

30. p 1 p 2 p 3

31. Color matching experiment 1 p 1 p 2 p 3

32. Color matching experiment 1 p 1 p 2 p 3 The primary color amounts needed for a match

33. Color matching experiment 2

34. p 1 p 2 p 3

35. Color matching experiment 2 p 1 p 2 p 3

36. Color matching experiment 2 p 1 p 2 p 3 We say a “negative” amount of p 2 was needed to make the match, because we added it to the test color’s side. The primary color amounts needed for a match: p 1 p 2 p 3

37. Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

38. Grassman’s Laws For color matches: –symmetry: U=V V=U –transitivity: U=V and V=W => U=W –proportionality: U=V tU=tV –additivity: if any two (or more) of the statements U=V, W=X, (U+W)=(V+X) are true, then so is the third These statements are as true as any biological law. They mean that additive color matching is linear. Forsyth & Ponce

39. Color Matching Functions p 1 = 645.2 nm p 2 = 525.3 nm p 3 = 444.4 nm

40. Since we can define colors using almost any set of primary colors, let ’ s agree on a set of primaries and color matching functions for the world to use …

41. CIE XYZ color space Commission Internationale d ’ Eclairage, 1931 “… as with any standards decision, there are some irratating aspects of the XYZ color-matching functions as well … no set of physically realizable primary lights that by direct measurement will yield the color matching functions. ” “ Although they have served quite well as a technical standard, and are understood by the mandarins of vision science, they have served quite poorly as tools for explaining the discipline to new students and colleagues outside the field. ” Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

42. CIE XYZ: Color matching functions are positive everywhere, but primaries are “imaginary” (require adding light to the test color’s side in a color matching experiment). Usually compute x, y, where x=X/(X+Y+Z) y=Y/(X+Y+Z) Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

43. A qualitative rendering of the CIE (x,y) space. The blobby region represents visible colors. There are sets of (x, y) coordinates that don’t represent real colors, because the primaries are not real lights (so that the color matching functions could be positive everywhere). Forsyth & Ponce

44. CIE chromaticity diagram encompasses all the perceivable colors in 2D space (x,y) by ignoring the luminance

45. A plot of the CIE (x,y) space. We show the spectral locus (the colors of monochromatic lights) and the black- body locus (the colors of heated black-bodies). I have also plotted the range of typical incandescent lighting. Forsyth & Ponce

46. Pure wavelength in chromaticity diagram Blue: big value of Z, therefore x and y small

47. Pure wavelength in chromaticity diagram Then y increases

48. Pure wavelength in chromaticity diagram Green: y is big

49. Pure wavelength in chromaticity diagram Yellow: x & y are equal

50. Pure wavelength in chromaticity diagram Red: big x, but y is not null

51. Color Gamut The color gamut for n primaries in CIE chromaticity diagram is the convexhull of the color positions

52. Color Gamut

53. Complementary Colors Illuminant C (Average sunlight)

54. Dominant Wavelength The spectral color which can be mixed with white light in order to reproduce the desired color C 2 have spectral distributions with subtractive dominant wave lengths

55. CIE color space Can think of X, Y, Z as coordinates Linear transform from typical RGB or LMS Always positive (because physical spectrum is positive and matching curves are positives) Note that many points in XYZ do not correspond to visible colors!

56. Color Gamut of RGB

57. XYZ vs. RGB Linear transform XYZ is rarely used for storage There are tons of flavors of RGB –sRGB, Adobe RGB –Different matrices! XYZ is more standardized XYZ can reproduce all colors with positive values XYZ is not realizable physically !! –What happens if you go “ off ” the diagram –In fact, the orthogonal (synthesis) basis of XYZ requires negative values.

58. RGB color space RGB cube –Easy for devices –But not perceptual –Where do the grays live? –Where is hue and saturation?

59. HSV Hue, Saturation, Value (Intensity) –RGB cube on its vertex Decouples the three components (a bit) Use rgb2hsv() and hsv2rgb() in Matlab

60. Color names for cartoon spectra 400 500 600 700 nm red green blue 400 500 600 700 nm cyan magenta yellow 400 500 600 700 nm

61. Additive color mixing 400 500 600 700 nm red green Red and green make… 400 500 600 700 nm yellow Yellow! When colors combine by adding the color spectra. Example color displays that follow this mixing rule: CRT phosphors, multiple projectors aimed at a screen, Polachrome slide film.

62. Simplified rendering models: reflectance Often are more interested in relative spectral composition than in overall intensity, so the spectral BRDF computation simplifies a wavelength-by-wavelength multiplication of relative energies..* = Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

63. Subtractive color mixing When colors combine by multiplying the color spectra. Examples that follow this mixing rule: most photographic films, paint, cascaded optical filters, crayons. 400 500 600 700 nm cyan yellow 400 500 600 700 nm Cyan and yellow (in crayons, called “blue” and yellow) make… 400 500 600 700 nm Green! green

64. NTSC color components: Y, I, Q

65. NTSC - RGB

66.  subtractive model (colors of pigments are subtracted)  used in color output devices  CMYK color model - K for black ink for reducing the amount of ink  CMY color model

67. Uniform color spaces McAdam ellipses (next slide) demonstrate that differences in x,y are a poor guide to differences in color Construct color spaces so that differences in coordinates are a good guide to differences in color. Forsyth & Ponce

68. Variations in color matches on a CIE x, y space. At the center of the ellipse is the color of a test light; the size of the ellipse represents the scatter of lights that the human observers tested would match to the test color; the boundary shows where the just noticeable difference is. The ellipses on the left have been magnified 10x for clarity; on the right they are plotted to scale. The ellipses are known as MacAdam ellipses after their inventor. The ellipses at the top are larger than those at the bottom of the figure, and that they rotate as they move up. This means that the magnitude of the difference in x, y coordinates is a poor guide to the difference in color. Forsyth & Ponce

69. Perceptually Uniform Space: MacAdam In perceptually uniform color space, Euclidean distances reflect perceived differences between colors MacAdam ellipses (areas of unperceivable differences) become circles Non-linear mapping, many solutions have been proposed Source: [Wyszecki and Stiles ’82]

70. CIELAB (a.k.a. CIE L*a*b*) Source: [Wyszecki and Stiles ’82] The reference perceptually uniform color space L: lightness a and b: color opponents X 0, Y 0, and Z 0 are used to color- balance: they ’ re the color of the reference white

71. Color Sensing in Camera (RGB) 3-chip vs. 1-chip: quality vs. cost Why more green? http://www.coolditionary.com/words/Bayer-filter.wikipedia http://www.cooldictionary.com/words/Bayer-filter.wikipedia Why 3 colors?

72. Practical Color Sensing: Bayer Grid Estimate RGB at ‘G’ cels from neighboring values http://www.cooldictionary.com/ words/Bayer-filter.wikipedia

73. White Balance Chromatic adaptation –If the light source is gradually changed in color, humans will adapt and still perceive the color of the surface the same

74. Color Temperature Blackbody radiators