1. Why is this hard to read

3. Unrelated vs. Related Color Unrelated color: color perceived to belong to an area in isolation (CIE 17.4) Related color: color perceived to belong to an area seen in relation to other colors (CIE 17.4)

4. Illusory contour Shape, as well as color, depends on surround Most neural processing is about differences

5. Illusory contour

6. CS 768 Color Science Perceiving color Describing color Modeling color Measuring color Reproducing color

8. Spectral measurement Measurement p( ) of the power (or energy, which is power x time ) of a light source as a function of wavelength Usually relative to p(560nm) Visible light 380-780 nm

11. Retinal line spread function retinal position relative intensity

12. Linearity additivity of response (superposition) r(m 1 +m 2 )=r(m 1 )+r(m 2 ) scaling (homogeneity) r(  m)=  r(m) r(m 1 (x,y)+m 2 (x,y))= r(m 1 )(x,y)+r(m 2 )(x,y)= (r(m 1 )+r(m 2 ))(x,y) r(  m(x,y))=  r(m)(x,y) retinal intensity monitor intensity

13. Non-linearity

15. http://webvision.med.utah.edu/

16. Ganglion Bipolar Amacrine Rod Cone Epithelium Optic nerve Retinal cross section Light Horizontal

17. Visual pathways Three major stages –Retina –LGN –Visual cortex –Visual cortex is further subdivided http://webvision.med.utah.edu/Color.html

18. Optic nerve 130 million photoreceptors feed 1 million ganglion cells whose output is the optic nerve. Optic nerve feeds the Lateral Geniculate Nucleus approximately 1-1 LGN feeds area V1 of visual cortex in complex ways.

19. Photoreceptors Cones - –respond in high (photopic) light –differing wavelength responses (3 types) –single cones feed retinal ganglion cells so give high spatial resolution but low sensitivity –highest sampling rate at fovea

20. Photoreceptors Rods –respond in low (scotopic) light –none in fovea try to foveate a dim star—it will disappear –one type of spectral response –several hundred feed each ganglion cell so give high sensitivity but low spatial resolution

21. Luminance Light intensity per unit area at the eye Measured in candelas/m 2 (in cd/m 2 ) Typical ambient luminance levels (in cd/m 2 ): –starlight 10 -3 –moonlight 10 -1 –indoor lighting 10 2 –sunlight 10 5 –max intensity of common CRT monitors 10 ^2 From Wandell, Useful Numbers in Vision Science http://white.stanford.edu/~brian/numbers/numbers.html

22. Rods and cones Rods saturate at 100 cd/m 2 so only cones work at high (photopic) light levels All rods have the same spectral sensitivity Low light condition is called scotopic Three cone types differ in spectral sensitivity and somewhat in spatial distribution.

23. Cones L (long wave), M (medium), S (short) –describes sensitivity curves. “Red”, “Green”, “Blue” is a misnomer. See spectral sensitivity.

25. Receptive fields Each neuron in the visual pathway sees a specific part of visual space, called its receptive field Retinal and LGN rf’s are circular, with opponency; Cortical are oriented and sometimes shape specific. - - - - - - -- - + - - On center rfRed-Green LGN rf + + + + + + + + - - - Oriented Cortical rf

27. Channels: Visual Pathways subdivided Channels Magno –Color-blind –Fast time response –High contrast sensitivity –Low spatial resolution Parvo –Color selective –Slow time response –Low contrast sensitivity –High spatial resolution Video coding implications Magno –Separate color from b&w –Need fast contrast changes (60Hz) –Keep fine shading in big areas –(Definition) Parvo –Separate color from b&w –Slow color changes OK (40 hz) –Omit fine shading in small areas –(Definition) (Not obvious yet) pattern detail can be all in b&w channel

29. Trichromacy Helmholtz thought three separate images went forward, R, G, B. Wrong because retinal processing combines them in opponent channels. Hering proposed opponent models, close to right.

30. Opponent Models Three channels leave the retina: –Red-Green (L-M+S = L-(M-S)) –Yellow-Blue(L+M-S) –Achromatic (L+M+S) Note that chromatic channels can have negative response (inhibition). This is difficult to model with light.

31. +- +

34. 100 10 1.0 0.1 0.001 012 Log Spatial Frequency (cpd) Contrast Sensitivity Luminance Red-Green Blue-Yellow

35. Color matching Grassman laws of linearity: (     )(   (   (   Hence for any stimulus s( ) and response r( ), total response is integral of s( ) r( ), taken over all or approximately  s( )r( )

36. Primary lights Test light Bipartite white screen Surround field Test lightPrimary lights Subject Surround light

37. Color Matching Spectra of primary lights s 1 ( ), s 2 ( ), s 3 ( ) Subject’s task: find c 1, c 2, c 3, such that c 1 s 1 ( )+c 2 s 2 ( )+c 3 s 3 ( ) matches test light. Problems (depending on s i ( )) –[c 1,c 2,c 3 ] is not unique (“metamer”) –may require some c i <0 (“negative power”)

38. Color Matching Suppose three monochromatic primaries r,g,b at 645.16, 526.32, 444.44 nm and a 10° field (Styles and Burch 1959). For any monochromatic light t( ) at  find scalars R=R(  G=G(  B=B(  such that t( ) = R(  r  G(  g  B(  b R( ,  G( ,  B(  are the color matching functions based on r,g,b.

40. Color matching Grassman laws of linearity: (     )(   (   (   Hence for any stimulus s( ) and response r( ), total response is integral of s( ) r( ), taken over all or approximately  s( )r( )

41. Color matching What about three monochromatic lights? M( ) = R* R ( ) + G* G ( ) + B* B ( ) Metamers possible good: RGB functions are like cone response bad: Can’t match all visible lights with any triple of monochromatic lights. Need to add some of primaries to the matched light

42. Primary lights Test light Bipartite white screen Surround field Test lightPrimary lights Subject Surround light

43. Color matching Solution: CIE XYZ basis functions

45. Color matching Note Y is V( ) None of these are lights Euclidean distance in RGB and in XYZ is not perceptually useful. Nothing about color appearance

46. XYZ problems No correlation to perceptual chromatic differences X-Z not related to color names or daylight spectral colors One solution: chromaticity

47. Chromaticity Diagrams x=X/(X+Y+Z) y=Y/(X+Y+Z) z=Z/(X+Y+Z) Perspective projection on X-Y plane z=1-(x-y), so really 2-d Can recover X,Y,Z given x,y and on XYZ, usually Y since it is luminance

48. Chromaticity Diagrams No color appearance info since no luminance info. No accounting for chromatic adaptation. Widely misused, including for color gamuts.

49. Some gamuts SWOP ENCAD GA ink

52. MacAdam Ellipses JND of chromaticity Bipartite equiluminant color matching to a given stimulus. Depends on chromaticity both in magnitude and direction.

54. MacAdam Ellipses For each observer, high correlation to variance of repeated color matches in direction, shape and size –2-d normal distributions are ellipses –neural noise? See Wysecki and Styles, Fig 1(5.4.1) p. 307

55. MacAdam Ellipses JND of chromaticity –Weak inter-observer correlation in size, shape, orientation. No explanation in Wysecki and Stiles 1982 More modern models that can normalize to observer?

56. MacAdam Ellipses JND of chromaticity –Extension to varying luminence: ellipsoids in XYZ space which project appropriately for fixed luminence

57. MacAdam Ellipses JND of chromaticity –Technology applications: Bit stealing: points inside chromatic JND ellipsoid are not distinguishable chromatically but may be above luminance JND. Using those points in RGB space can thus increase the luminance resolution. In turn, this has appearance of increased spatial resolution (“anti-aliasing”) Microsoft ClearType. See http://www.grc.com/freeandclear.htm and http://www.ductus.com/cleartype/cleartype.html

58. Complementary Colors Colors which sum to white point are called complementary colors a*c1+b*c2 = wp Some monochromatic colors have complements, others don’t. See ComplementaryColors.m Complements may be out of gamut. See Photoshop.