Color Principles for Computer Graphics Donald House 9/17/09 Artists slides by Lynette House

Physics of Color

Physiology of Color

3 cone response curves Luminance efficiency

Artists Color Hue Saturation Value

Luminance Dark to Light Value range –High key –Middle key –Low key

Hue - Paint Mixing Physical mix of opaque paints Primary: RYB Secondary: OGV Neutral: R + Y + B

Hue - Ink Mixing Subtractive mix of transparent inks Primary: CMY Secondary: RGB ~Black: C + M + Y Actually use CMYK to get true black

Hue - Ink Mixing Assumption: ink printed on pure white paper CMY = White – RGB: C = 1 – R, M = 1 – G, Y = 1 – B CMYK from CMY (K is black ink): K = min(C, M, Y) C = C – K, M = M – K, Y = Y - K

Hue - Light Mixing Additive mix of colored lights Primary: RGB Secondary: CMY White = R + G + B Show demonstration of optical mixing

Saturation Purity of color

Perception of Color In the end, color is a perceptual phenomenon

Color Constancy Perceived color is highly context dependent Allowing color recognition with variable lighting conditions

Color Constancy Perceived color is highly context dependent Allowing color recognition with variable lighting conditions

Simultaneous Contrast

RGB Color Wheel Warm/Cool Complements Split Complement Analogous Show RGB Cube

HSV Color Model HSV is a projection of the RGB space RGB cubeHSV top viewHSV cone

HSV Color Model Hue, an angular measure (0 … 360)

HSV Color Model Saturation, a fractional measure (0.0 … 1.0)

HSV Color Model Value, a fractional measure (0.0 … 1.0)

CIE 1931 Study Color Matching Experiment

CIE Color Matching Functions CIE RGB Matching Functions CIE XYZ Matching Functions

RGB from Spectrum RGB = x d

XYZ from Spectrum XYZ = x d

CIE xyY color model is used to catalog colors: x = X / (X + Y + Z) y = Y / (X + Y + Z) Y = luminance CIE xyY from CIE XYZ

CIE xyY Color Cone

CIE xyY Typical Display Gamut

CIE Lu * v * and Lab Perceptually Uniform Spaces Lu * v * rescales xyY Lab color opponents

Color Picker

End