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Part 5: Reproduction Principles and Related Color Spaces

Additive Color Reproduction:

Colors are created by adding outputs of three primaries (TV, Laser displays, …)

Typical primaries: Red, Green, Blue

Basic color reproduction principles/Additive

Basic color reproduction principles/Subtractive

Subtractive Color Reproduction:

Colors are created by subtraction of colors from underlying white (printing,…)

Typical primaries: Cyan, Magenta, Yellow

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RGB & CMY(K)-Space• RGB = Red-Green-Blue

• CMY = Cyan-Magenta-Yellow

• CMYK = Cyan-Magenta-Yellow-Black

RGB for reproduction on additive devices like monitorsalso used for cameras

CMY(K) for subtractive devices like printers

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−−−

=⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

YMC

BGR

111

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−−−

=⎟⎟⎟

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⎞

⎜⎜⎜

⎝

⎛

BGR

YMC

111

• RGB -> CMY:

•CMY -> RGB:

Simple relation between RGB and CMY

Conversion between CMY and CMYK

⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

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−−−−−−

=

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KKYKKM

KKC

YMC

YMCK

1

1

1

),,min(

3

3

3

333

4

4

4• CMY -> CMYK:

• CMYK -> CMY⎟⎟⎟

⎠

⎞

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⎝

⎛

+−⋅+−⋅+−⋅

=⎟⎟⎟

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⎛

KKYKKMKKC

YMC

)1()1()1(

4

4

4

3

3

3

BUT:

In reality this ismuch more complicated!

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Device dependency

The color represented by R, G, B, C, M, Y, Kdepend on the devices used,i.e the characteristics of the monitor channels, the camera or the inks

Conversion between RGB and XYZ

ITU-RGB709 is a standard describing studio cameras(ITU = International Telecommunications Union)

⎟⎟⎟

⎠

⎞

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BGR

ZYX

05.9592.1193.122.752.7126.2105.1876.3524.41

X,Y,Z system assumes D65 daylight

Part 6: Some Color Measurement Devices

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Today mainly CCD cameras with:

• one sensor array and RGB-filters

•three sensor arrays with beam-splitter

Traditional photographic film

(3-12+light sensitive layers)

BEAMSPLITTER

R

G

B

Color Detectors (Cameras)

Foveon

Sony Cybershot DSC-F828

RGB RGBE

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New Trends- MultispectralCameras

EU project Crisatel:

ENST Paris, Louvre, National Gallery …Linear CCD 12000 pixels

Moving frame with 30000 linesMultispectral filters: 13 bands

Resolution: 16 bits/pixelFile size: 9.4 Gb

Product: http://www.jumboscan.com/

Used in scanners to compare the actual (transparency, reflectance)

to the ideal (transparency, reflectance)

Often optimised to objects (inks etc.)

Filter

SourceSource

Amplifier

Detector

Probe

Filter

SourceMeasure with probe Compare to measurement without

probe

Color Detectors (Densitometers)

Characteristic of Status A densitometer

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Spectroradiometers PhotoResearch

The PR-705 SpectraScan® SpectraRadiometer®

Spectrolino

Digital Cameras as Measurement Device

ExpansiveNeeds experienceSingle Point Measurements

Cheaper8-12M Measurements Needs calibrationhttp://www.diva-portal.org/diva/getDocument?urn_nbn_se_liu_diva-2667-1__fulltext.pdfExjobb: Martin Solli: Filter characterization in digital cameras

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Computational Photography

Lightstage...

Part 7: More Color Vision

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• Hue: red-green-blue-yellow-etc

• Brightness: how bright an object appears

• Colorfulness: how much white is included

• Lightness = normed brightness = Brightness/Brightness(White)

• Chroma = normedColorfulness=Colorfulness/Brightness(White)

• Saturation=Colorfulness/Brightness

• (un-)Related colors: unrelated color of an object belongs to an area independent of other colors

Color vocabulary

Perception phenomena not handled bycolorimetry

Simultaneous contrast: The color of a point depends not only on its physical spectrum but also on the background on which it is seen

Color perception depends on the whole configuration

More simultaneous contrast

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Magnitude of color difference is larger if the stimuli are shown on background with color similar to the stimuli

Crispening

Depth perception of red-blue

Blue text on a red background is hard to read

Red text on a blue background is easier to read

Black text on a white background is best

Red/Blue Text

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1. Spreading: Small stimuli are fused (half-toning). Just above the limit there is a region where only the colors blend but where the objects are perceived as different

2. Bezold-Brücke effect: Hue depends on luminance! Viewing monochromatic stimuli under different luminance changes the hue

3. Abney effect: Constant hue curves are not lines! Mixing monochromatic light with different amounts of white light changes the hue.

4. Helmholtz-Kohlrausch effect: Brightness increases with saturation!Brightness depends on luminance and chrominance.

5. Hunt effect: Increasing luminance leads to increasing colorfulness!6. Stevens effect: Contrast increases with luminance! With increasing

luminance dark becomes darker and light becomes lighter

:

More effects

The human eye (again)

A) Connection between retina and brain is a bottleneck

(Many more sensors than nerves to brain)

Therefore compression is needed!

B) All cells involved adapt to changing conditions

Continued high stimulation lower sensitivity

Continued low stimulation increasing sensitivity

Post-detection processes

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L

M

S YB

RG

BW

BW = L+M+S RG = L-M YB = S-M-L

Spectral processing (opponent color coding)

Single sensor signals from a region are combined

G-

R+

-

+

Spatial signal processing on the retina

• Oriented Edges

• Oriented Bars

• Input from one/two eye(s), stereo

• Spatial frequencies

• Temporal frequencies

• Combination of all these features

Other “Detectors”

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Human vision system adapts, it has a tendency to go back to a stable state

Examples:

General brightness adaptation

Lateral brightness adaptation

Chromatic adaptation

…

Adaptation

Adaptation to overall light intensity

absolute intensity influences contrast and colorfulness

Prints appear different in indoor and outdoor conditions even if the viewer is adapted to the light conditions

General brightness adaptation

Brightness at a point depends also on response from neighboring points

Glowing axes, stairs, crispening ….

Practical application: Slides on a screen in a dark room appear different from the same image on a screen in daylight

Lateral brightness adaptation

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Hermann Grid

“Rotating Snake”

<http://www.ritsumei.ac.jp/~akitaoka/rotsnake.gif> by Akiyoshi Kitaoka

Longer stimulation of a channels decreases its sensitivity

Demonstrated by different versions of the afterimage

Von Kries adaptation = different scaling for different channels

Chromatic adaptation

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Mean Normalization

Compensate such that mean is constant.

Max Normalization

Compensate such that the point with maximum intensity is white

Mean Result

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Scaling

mean-Kries white-Kries

Some recent resultsInput: Multi-spectral image with 31 channelsIllumination: Planck SpectraCamera: Canon Calibrated

Experiment 1: Find RGB transforms that stabilize the imageApplication Industrial Inspection

Experiment 2: Find RGB transform that predicts the imageApplication Movie industry-Relighting

• Memory color: We “KNOW” the color of many objects

• Color constancy: We know that objects usually don’t change colors (involves memory color and chromatic adaptation)

• Discounting the illuminant: May often know the characteristics of the illuminant

• Object recognition: Filling-in, induction, memory, learning etc.

Other visual mechanism relevant for human vision

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Compensation Scene 6

Multispectral Simulation RGB Compensation

Simulation Scene 8

Multispectral Simulation RGB Simulation

Compensation

Multispectral Simulation RGB Compensation

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Simulation

Multispectral Simulation RGB Simulation

Application: Automatic Color Correction

Bad example

Application: Automatic Color Correction

Sorted

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Intelligent Color

Correction (1)

Segment image in

Object and background

Intelligent Color

Correction (2)

Part 8: Color in Matlab

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Color Spaces

How it worksI_rgb = imread('peppers.png');

Create a color transformation structure. A color transformation structure defines the conversion between two color spaces. You use the makecform function to create the structure, specifying a transformation type string as an argument. This example creates a color transformation structure that defines a conversion from RGB color data to XYZ color data.

C = makecform('srgb2xyz');

Perform the conversion. You use the applycform function to perform the conversion, specifying as arguments the color data you want to convert and the color transformation structure that defines the conversion. The applycform function returns the converted data.

I_xyz = applycform(I_rgb,C);

C 1x1 7744 struct arrayI_xyz 384x512x3 1179648 uint16 arrayI_rgb 384x512x3 589824 uint8 array

Data Types

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Part 9: Color Management Systems

Typical color image handling:

• Take slide with camera

• Scan the slide store on file

• Edit file on the monitor

• Convert file and print

Some factors which influence the result:

• Illumination conditions

• Camera

• Film-type

• Chemical processing of film

• Scanner characteristics

• Monitor characteristics

• Software implementations

• Printer characteristics

• Ink properties

• Paper type

Why Color Management?

• Try to produce an output which is colorimetric as similar to the input as possible

• Try to convert all images involved to a common set of colors (for example printer colors)

• Try to keep as much information about each module as possible Practical problems

• Each device has a color set it can handle (Color gamut)

• File formats

• Data compression

• Color co-ordinates

• ...

Basic strategies/Practical problems

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ICC was founded 1993 by companies from Computer, Printer and Software business Treatment of color is done by Operating System

Devices are characterized by profiles

Tables, programs etc. which describe the device

Several color spaces are supported as standard (CIEXYZ, CIELAB)

Profiles describe

• Input devices

• Output devices

• Display devices

• Color space conversions

• Other profiles

ICC = International Color Consortium

A basic color management system connects input and output profiles by converting color information from input to output device

Basic Color Management Systems

Take into account input/output viewing conditions …….

Advanced Color Management Module

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There are two main strategies to describe input/output devices

• Look-up-tables (LUT) describing input-output relations by a table

• Analytical descriptions:

White point and

Gamma value and

Black point

Example:

output = a * exp(c*x) + b

Characterization of input and output devices

Generation of a profile for a scanner is done by

Scanning in a test image

Mapping result into common color space (Profile Connection Space, PCS) and

Comparison of measured an reference values

Generation of printer profiles is done by printing standard patches and

measuring the resulting images

Several samples should be measured and averaged to get reliable results

Generating Profiles

Important problem in color management:

Different devices have different gamuts, i.e sets of colors they can produce

Moving color images between different devices needs decision what to do with non-existing colors.

Gamut mapping

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Gamuts for different systems

ITN: Color Related Research

Modelling:CamerasIlluminationsInteractions

Image

Scene=objects+illuminationsDigital camera

Estimation of camera sensitivity

Illumination +Camera

ExamplePlanckSpectrum

ParameterTemperature

Small continuous changesare hardly visibleColor Constancy