Ip15 color - אוניברסיטת חיפה

53
Color Representation

Transcript of Ip15 color - אוניברסיטת חיפה

Page 1: Ip15 color - אוניברסיטת חיפה

Color Representation

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Electromagnetic Radiation -Spectrum

Gamma X rays Infrared Radar FM TV AMUltra-violet

10-12

10-8

10-4

104

1 108

electricityACShort-

wave

400 nm 500 nm 600 nm 700 nmWavelength in nanometers (1nm=10-9 m)

Wavelength in meters (m)

Visible light

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The Spectral Power Distribution (SPD) of a light is a function f(λ) which defines the energy at each wavelength.

Wavelength (λ)400 500 600 700

0

0.5

1

Rel

ativ

e P

ower

Spectral Power Distribution

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Examples of Spectral power Distributions

Blue Skylight Tungsten bulb

Red monitor phosphor Monochromatic light

400 500 600 7000

0.5

1

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0.5

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400 500 600 7000

0.5

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0.5

1

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+ -

+ -

+ -

test match

Color Matching ExperimentThree primary lights are set to match a test light.

=~

Test light Match light

400 500 600 7000400 500 600 7000

Pow

er

Metamer - two lights that appear the same visually. They might have different SPDs (spectral power distributions).

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Trichromatic Color Theory

Thomas Young (1773-1829) -A few different retinal receptors operating with different wavelength sensitivities will allow humans to perceivethe number of colors that they do.Suggested 3 receptors.

Helmholtz & Maxwell (1850) -Color matching with 3 primaries.

“tri”=three “chroma”=colorcolor vision is based on three primaries

(i.e., it is 3 dimensional).

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The Human Eye

Optic NerveFovea

Vitreous

Optic Disc

Lens

Pupil

Cornea

Ocular MuscleRetina

Humor

Iris

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The Human Retina

light

rods cones

horizontal

amacrine

bipolar

ganglion

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Cones -

Wavelength (nm)

Rel

ativ

e se

nsiti

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Retinal Photoreceptors

Cone Spectral Sensitivity

400 500 600 7000

0.25

0.5

0.75

1

• High illumination levels (Photopic vision)• Less sensitive than rods.• 5 million cones in each eye.• Only cones in fovea (aprox. 50,000).• Density decreases with distance from fovea.• 3 cone types differing in their spectral

sensitivity: L , M, and S cones.

LMS

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Linear Color Spaces

Colors in 3D color space can be described as linear combinations of 3 basis colors:

primaries

a• + b• + c•=

The representation of :

is then given by: (a, b, c)

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Rgb Image

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black

redgreen

blue

white

The RGB Cube

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Color Edge Detection

Original

R - Edges G - Edges

B - Edges All - Edges

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Color Edge Detection

Original

R - Edges G - Edges

B - Edges All - Edges

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RGB Color Cube

R

G

B

Brightness

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HueSaturation

Brightness

Black

White

RG

B

Color Description

Hue (red, green, yelow, blue ...)

Saturation (pink,bright red, ....)

Lightness (black, grey, white ....)

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MayuraDraw

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PowerPoint

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PhotoShop

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YIQ - Color Space

NTSC = National Television Systems Committee

Y = luminanceI = red-greenQ = blue-yellow

RGB

=YIQ

0.177 0.813 0.0110.540 -0.263 -0.1740.246 -0.675 0.404

R G B are the CIE-RGB

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RGB To Monochrome

RGB

Y

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Original Y - Blur

I - Blur Q - Blur

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Subtractive Color System - CMYK

Cyan

Magenta

Yellow

blacK

= removes red

= removes green

= removes blue

= removes all

Printer Dyes:

cyan magenta yellow

B G R B G R B G R

trans

mit

Ideal block dyes:

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Opponent Color Wheel

Additive primariesSubtractive Primaries

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yellow

B G R

Multiplicative (Subtractive) Color System

red = magenta + yellow

magenta

B G R

red

R

B G R*

=

B G R

= magenta + yellow= cyan + yellow= magenta + cyan

redgreenblue

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Cyan - controls amount of red in print:

cyan

B G R

low C = high R (also high G and B)high C = low R (high G and B)

R G BR G BR G BHigh density

cyanMedium density

cyanLow density

cyan

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CMY + Black

C + M + Y = K (black)

• Using three inks for black is expensive• C+M+Y = dark brown not black• Black instead of C+M+Y is crisper with more

contrast.

100 50 70

Undercolor removal -(gray component replacement)

=

50 0 2050

+

C M Y C M YK

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R G R G R G R GG B G B G B G BR G R G R G R GG B G B G B G BR G R G R G R GG B G B G B G BR G R G R G R GG B G B G B G B

R R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R R

G G G G G G G GG G G G G G G GG G G G G G G GG G G G G G G GG G G G G G G GG G G G G G G GG G G G G G G GG G G G G G G G

B B B B B B B BB B B B B B B BB B B B B B B BB B B B B B B BB B B B B B B BB B B B B B B BB B B B B B B BB B B B B B B B

demosaic

Demosaicing

Digital camera (Kodak DS40)

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Demosaicing - Linear interpolation

R R R R

R R R R

R R R R

R R R R

R R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R RR R R R R R R R

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R G R G R G R G B G B G B GR G R G R G R G B G B G B GR G R G R G R G B G B G B G

R image

G G G G G G G

G G G G G G G

G G GG G G G

R R R R

R R R R

R R R R

B B B

B B B

B B B

G image B image

G G G G G G GG G G G G G GG G G G G G GG G G G G G GG G G G G G GG G G G G G G

R R R R R R RR R R R R R RR R R R R R R R R R R R R R R R R R R R R R R R R R R R

B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B

interpolate interpolate

Demosaic Aliasing

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Demosaicing - Example (Kodak)

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Demosaicing - Various Approaches

Regularization

Minimize over a functional with a data fit termand an inter-channel color correlation term.

Minimal Surface

Minimize over a functional with a data fit termand a 5D surface area term. (Beltrami Flow)

100 50

Grayscale Image

RGB Image

x

y

B

GR

5D

3D

(Gamer & Keren)

(Kimmel)

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Learning SchemesLearn linear and non linear optimal filtersfor classes of images (ANN).

(Kapah & Hel-Or)

Demosaicing - Various Approaches

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Quadratic - Learned on Image

Channel independent Perceptron (Linear)

Demosaicing - Learning Schemes

Quadratic - Learned on Class

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Color Quantization

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Indexed Image

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Image Independent Quantization

original gray value

quantizedgray value

0 64 128 192 255

32 96 160 224

original gray value

quantizedgray value

0 64 128 192 255

32 96 160 224

original gray value

quantizedgray value

0 64 128 192 255

32 96 160 224

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Image Independent Quantization

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Original 125 Colors

64 Colors 27 Colors

Image Independent Quantization

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RGB Space

RGB Image

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Image Dependent Quantization

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Clustering using Iso Data

Input: C={ci}, i=1..n - color points.Output: S={sj}, j=1..k - color indices.

• Distribute sj, j=1..k, uniformly in color space.

• Divide C into k classes based of distances to S.

• For each class j, calculate the mean Mj.

• Set Sj=Mj.• Iterate until convergence.

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Original

Image Dependent Quantization

Independent Dependent

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Albers (1975)