CS 445 / 645 Introduction to Computer Graphics

1. CS 445 / 645Introduction to Computer Graphics Lecture 13 Color

2. Assignment 3 • Due March 23 • Fourth years plan for thesis collision • We’ll provide lots of details to keep the assignment doable • Morphing algorithm

3. Last Class • We discussed vision physiology andperception of gradients • Today: • Color perception • Color representations

4. Specifying Color • Color perception usually involves three quantities: • Hue: Distinguishes between colors like red, green, blue, etc • Saturation: How far the color is from a gray of equal intensity • Lightness: The perceived intensity of a reflecting object • Sometimes lightness is called brightness if the object is emitting light instead of reflecting it. • In order to use color precisely in computer graphics, we need to be able to specify and measure colors.

5. Combining Colors Additive (RGB) Shining colored lightson a white ball Subtractive (CMYK) Mixing paint colors andilluminating with white light

6. How Do Artists Do It? • Artists often specify color as tints, shades, and tones of saturated (pure) pigments • Tint: Gotten by adding white to a pure pigment, decreasing saturation • Shade: Gotten byadding black to a pure pigment, decreasing lightness • Tone: Gotten by adding white and black to a pure pigment White Tints Pure Color Tones Grays Shades Black

7. HSV Color Space • Computer scientists frequently use an intuitive color space that corresponds to tint, shade, and tone: • Hue - The color we see (red, green, purple) • Saturation - How far is the color from gray (pink is less saturated than red, sky blue is less saturated than royal blue) • Brightness (Luminance) - How bright is the color (how bright are the lights illuminating the object?)

8. HSV Color Model • Hue (H) is the angle around the vertical axis • Saturation (S) is a valuefrom 0 to 1 indicatinghow far from the verticalaxis the color lies • Value (V) is the height of the hexcone”

9. HSV Color Model H S V Color 0 1.0 1.0 Red 120 1.0 1.0 Green 240 1.0 1.0 Blue * 0.0 1.0 White * 0.0 0.5 Gray * * 0.0 Black 60 1.0 1.0 ? 270 0.5 1.0 ? 270 0.0 0.7 ? Figure 15.16&15.17 from H&B

10. Intuitive Color Spaces • A top-down view of hexcone

11. HSV Color Space • A more intuitive color space • H = Hue • S = Saturation • V = Value (or brightness) Saturation Value Hue

12. Precise Color Specifications • Pigment-mixing is subjective --- depends on human observer, surrounding colors, lighting of the environment, etc • We need an objective color specification • Light is electromagnetic energy in the 400 to 700 nm wavelength range • Dominant wavelength is the wavelength of the color we “see” • Excitation purity is the proportion of pure colored light to white light • Luminanceis the amount (or intensity) of the light

13. Electromagnetic Spectrum • Visible light frequencies range between ... • Red = 4.3 x 1014 hertz (700nm) • Violet = 7.5 x 1014 hertz (400nm) Figures 15.1 from H&B

14. Visible Light • Hue = dominant frequency (highest peak) • Saturation = excitation purity (ratio of highest to rest) • Lightness = luminance (area under curve) White Light Orange Light Figures 15.3-4 from H&B

15. How well do we see color? • What color do we see the best? • Yellow-green at 550 nm • What color do we see the worst? • Blue at 440 nm • Flashback: Colortables (colormaps) for color storage • Which RGB value gets the most bits? • Can perceive color differences of 10 nm at extremes (violet and red) and 2 nm between blue and yellow • Metamers – different energy radiations look like the same color • Color perception also affected by surrounding light and adaptation

16. Just noticeable difference (JND) • 128 fully saturated hues can be distinguished • Cannot perceive hue differences with less saturated light. • Sensitivity to changes in saturation for a fixed hue and brightness ranges from 16 to 23 depending on hue. • Talked about representing intensities last lecture

17. Human Color Vision • Humans have 3 light sensitive pigments in their cones, called L, M, and S • Each has a different spectral response curve: • This leads to metamerism • “Tristimulus” color theory

18. Color Spaces • Three types of cones suggests color is a 3D quantity. How to define 3D color space? • Idea: • Shine given wavelength () on a screen • User must control three lasers producing three wavelengths (say R=700nm, G=546nm, and B=436nm) • Adjust intensity of RGB until colors are identical • Note phosphors of TV are not perfect RGBemitters as the results to right demonstrate

19. A Problem Exists • Exact target match (l) with phosphors not possible • Some red had to be added to target color to permit exact match using “knobs” on RGB intensity output of CRT • Equivalently (theoretically), some red could have been removed from CRT output • Figure shows that red phosphor must remove some cyan for perfect match • CRT phosphors cannot remove cyan, so 500 nm cannot be generated

20. CIE Color Space • No standard set of three wavelengths can be combined to generate all other wavelengths. • The CIE(Commission Internationale d’Eclairage) defined three hypothetical lights X, Y, and Z with these spectra: • Idea: any wavelength  can be matched perceptually by positivecombinations of X, Y, and Z x ~ R y ~ G z ~ B

21. CIE Color Space • The gamut of all colors perceivable is thus a three-dimensional shape in X, Y, Z • Color = xX + yY + zZ

22. CIE Chromaticity Diagram (1931) • For simplicity, we often project to the 2D plane x + y + z = 1 • x = x / (x+y+z) • y = y / (x+y+z) • z = 1 – x - y

23. Device Color Gamuts • Since X, Y, and Z are hypothetical light sources, no real device can produce the entire gamut of perceivable color • Example: CRT monitor

24. Device Color Gamuts • We can use the CIE chromaticity diagram to compare the gamuts of various devices: • Note, for example, that a color printercannot reproduceall shades availableon a color monitor

25. A Problem With XYZ Colors • If we have two colors C1 and C2, and we add DC to both of them, the differences between the original and new colors will not be perceived to be equal • This is due to the variation of the just noticeable differences in saturated hues • XYZ space is not perceptually uniform • LUV space was created to address this problem

26. RGB Color Space (Color Cube) • Define colors with (r, g, b) amounts of red, green, and blue

27. RGB Color Gamuts • The RGB color cube sits within CIE color space something like this:

28. Converting Color Spaces • Simple matrix operation: • The transformation C2 = M-12 M1 C1yields RGB on monitor 2 that is equivalent to a given RGB on monitor 1

29. YIQ Color Space • YIQis the color model used for color TV in America. Y is brightness,I (orange-cyan) & Q (green-magenta) are color • Note: Yis the same as CIE’s Y • Result: Use the Y alone and backwards compatibility with B/W TV! • These days when you convert RGB image to B/W image, the green and blue components are thrown away and red is used to control shades of grey (usually)

30. Converting Color Spaces • Converting between color models can also be expressed as such a matrix transform: • Note the relative unimportance of blue in computing the Y

31. Perceptually Uniform Color Space • Color space in which Euclidean distance between two colors in space is proportional to the perceived distance • CIE, RGB, not perceptually uniform • Example with RGB • LUV was created to be perceptually uniform

32. The CMY Color Model • Cyan, magenta, and yellow are the complements of red, green, and blue • We can use them as filters to subtract from white • The space is the same as RGB except the origin is white instead of black • This is useful for hardcopy devices like laser printers • If you put cyan ink on the page, no red light is reflected • Add black as option (CMYK) to match equal parts CMY

33. Halftoning • A technique used in newspaper printing • Only two intensities are possible, blob of ink and no blob of ink • But, the size of the blob can be varied • Also, the dither patterns of small dots can be used

34. Halftoning

35. Halftoning – dot size

36. Halftoning – Moire Patterns • Repeated use of same dot pattern for particular shade results in repeated pattern • Perceived as a moire pattern • Instead, randomize halftone pattern

37. Dithering • Halftoning for color images