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  1. nineteen color Many thanks to Steve Eaton of the MIT Media Lab for inspiration and examples

  2. Seven traditional color contrasts Hue Light/dark Warm/cold Complements Simultaneous contrast Saturation Extension Color perception (review) Color spaces Color schemes Color design Today

  3. Spectrum vs color • Real photons come in continuous range of wavelengths • Real light hitting your eye has a continuous range of wavelengths • Each with a different intensity • Trichromacy • Your eye reduces all this information to just three dimensions of color • Metamerism • Florescent lights produce strange colors

  4. Trichromacy • Having different cones for every possible wavelength would be bad • We just have three kinds of cones • “Blue” cones: short wavelengths • “Green” cones: intermediate wavelengths • “Red” cones: long wavelengths • However, their responses overlap • The eye reduces all the wavelengths at a given pixel to just the total “amount” of “red”, “green”, and “blue”

  5. Components of a color image

  6. Evolution of the color system S to cortex

  7. Evolution of the color system S intensity + to cortex L chroma (B-Y) cool/warm −

  8. Color image Intensity (R+G+B) Chromaticity (B-Y) white = cool black = warm grey = neurtral Intensity and chromaticity

  9. Evolution of the color system S intensity + to cortex I chroma (B-Y) cool/warm − L chroma (R-G) −

  10. Color image Intensity Intensity and chromaticity Chromaticity (B-Y) white = cool black = warn grey = neutral Chromaticity (R-G) white = red black = green grey = balanced

  11. Code [define gray [i → [color i i i]]] [define signed-gray [i → [gray [+ 128 [∕ i 2]]]]] [define b-y [c → [− [blue c] [∕ [+ [red c] [green c]] 2]]]] [map-bitmap [c → [signed-gray [b-y c]]] cones]

  12. Code [define r-g [c → [− [red c] [green c]]]] [map-bitmap [c → [signed-gray [r-g c]]] cones]

  13. Colors can be arranged in different ways based on similarity Amount of red Amount of green Amount of blue Warmth Saturation Value (intensity, brightness) Brilliance These can be thought of as different dimensions or parameters of color Three dimensions suffices to arrange all colors But different dimensions can be chosen The result of choosing a set of dimensions is a color space Color spaces

  14. The LMS color space • This is the “real” color space used by your eye • Long-wavelength cone response • Medium wavelength cone response • Short wavelength cone response • Not generally used outside of neuroscience, however, because it’s hard to measure

  15. The RGB color space • The additive color space • This is the color space we’re used to thinking about in computers • Amount of “red” light • Amount of “green” light • Amount of “blue” light • The problem is that “red” varies from output device to output device • Color calibration

  16. Balancing primaries • Our ideas of “typical” reds, greens, and blues don’t actually balance out to white • In the WWW standards and on Windows • “red” = (255, 0, 0) • “green” = (0, 255, 0) • “blue” = (0, 0, 255) 255 128 255 255 255 255

  17. Complementary colors • Colors that sum to an achromatic color (white or neutral gray) • Red and cyan • Green and magenta • Blue and yellow • Make strong contrasts with one another • Achromatic colors are their own complements

  18. The CMY color space • The subtractive primaries • Fundamental primary colors for pigments • Each primary is the complement of an additive primary • Cyan (anti-red) • Magenta (anti-green) • Yellow (anti-blue) • CMYK is used in the printing industry • K (black ink) added for better reproduction of gray tones

  19. Traditional subtractive primaries • In traditional painting, the subtractive primaries • Are not cyan, magenta, and yellow • They’re red, blue, and yellow • The complements are • Red/green • Blue/orange • Yellow/violet • Blue and yellow paint make green! • Due to the fact that pigments operate on continuous wavelengths • Difference between • Combining pigments by mixing • Halftoning/pointilism

  20. The color wheel • Many versions, from Newton to Itten • Itten’s version • Start with 3 primaries • Mix adjacent primaries to make secondary colors • Recurse to make tertiary colors, etc.

  21. Using complementary colors • Can be placed together to create striking contrasts • Caveats • Complements in what color space? • Complementary colors also contrast in other dimensions • Yellow is much lighter than violet • Red-orange is much warmer than blue-green

  22. Contrast of complements Van Gogh, Night Cafe

  23. Contrastof complements Van Gogh, Bottle and Lemons

  24. Contrast of complements Jan van Eyck Madonna and the Chancellor Rolin

  25. Evolution of the color system S intensity + to cortex I chroma (B-Y) cool/warm − L chroma (R-G) −

  26. The opponent color space • Hering: Color can be “blue-green”, “yellow-red”, but not “yellow-blue”, or “green-red” • Opponent-process model • R-G channel • B-Y channel • Intensity channel (R+G+B) • Used in visual cortex • Computed in retinal ganglion • Used in image compression, NTSC video • Linear transform • Color more compressed

  27. R-G contrast • Effective contrast • Red and green hues have similar brilliance (at least according to some) • Arguably explains perception of green as complement of red Van Gogh, Night Café

  28. B-Y contrast • S cone response similar to rods • Used to depict night • Why not just paint black? • Because you can’t see any details • Arguably explains warm/cool distinction Van Gogh, Café at Night

  29. Contrast of warm and cold • Warm and cold colors can also be juxtaposed to create powerful contrasts • Most effective when • Colors are of medium brilliance • Equivalent brightness/value (otherwise it’s perceived as a brightness contrast)

  30. Warm-cold modulation

  31. Contrast of warm and cold Brekendrige, Philadelphia

  32. Contrast of warm and cold Monet, Parliament

  33. Contrast of warm and cold Van Gogh,14 Sunflowers in a Vase

  34. Contrast of warm and cold Monet, Sunrise

  35. Simultaneous contrast Bars “pull” squares toward theiropponent colors

  36. Simultaneous contrast

  37. YUV space • Roughly, • Y = R+G+B (intensity) • U=R-Y • V=G-Y • However, it’s tweaked in funny ways to make it more efficient for transmission • Y=.3R+.59G+.11B • Used in NTSC, jpeg, “component video”, DVD, mpeg

  38. Cylindrical color spaces • Basic idea: any color can be formed by: • Taking a “pure” color (color with no grey in it) • Primary or mixture of two primaries • “Diluting” it with grey • Adds in the third primary • Adjusting its overall intensity • Multiplying it in RGB space • Axes • Height: Intensity • Radius: amount of white • Angle: hue

  39. Cylindrical color spaces • Hue • The pure color from which you started • The “color” of the color • Saturation • The ratio of pure color to grey • Value (aka lightness, intensity, …) • Total amount of light (R+G+B)

  40. The HSV color space

  41. HSI space • True polar color space

  42. Converting RGB to HSV [define rgb→hsv[c → [with white = [min c.R c.G c.B] value = [max c.R c.G c.B] «deeply bogus» [with delta = [− value white] [if [= delta 0]«achromatic (gray) case» [list 0 0 value] «chromatic case» [with saturation = [if [= delta 0] 0 [ ⁄ delta value]] [list [hue c delta value] saturation value]]]]]]

  43. Converting RGB to HSV(what a sick kluge) [define hue[c delta value → [if [= value c.R] [× 60 «red is the strongest color» [ ⁄ [- c.G c.B] «so hue “angle” is green/blue difference» delta]] [if [= value c.G] [+ 120 [× 60 «green is the strongest color» [ ⁄ [- c.B c.R] «so blue-red difference» delta]] [+ 240 «blue is the strongest color» [× 60 «so red-green difference» [ ⁄ [- c.R c.G] delta]]]]]]

  44. Contrast of hue • Juxtaposition of colors of different hue • Degree of contrast is degree of hue difference • “Angle” between colors • Saturated primaries most intense • Secondary colors are less intense

  45. Contrast of hue Grunewald, Resurrection

  46. Contrast of hue Piet Mondrian, Composition A

  47. Contrast of saturation Bouguereau, A Soul Brought to Heaven

  48. Contrastof saturation Hendrick Terbrugghen The Calling of St Matthew

  49. Modulation of saturation by value

  50. Contrast of value • Chiaroscuro • See previous lecture • Lightness of background affects apparent saturation of a color • White background mutes • Black background intensifies • Different colors have different apparent brightnesses