color coordinate

8/3/2019 Color Coordinate

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Lecture 8. Color Image Processing

EL512 Image Processing

Dr. Zhu Liu

[email protected]

Note: Part of the materials in the slides are from Gonzalezs DigitalImage Processingand Onurs lecture slides

Fall 2004 EL512 Image Processing Lecture 8, Page 2

Lecture Outline

Color perception and representation

Human perception of color

Trichromatic color mixing theory

Different color representations

Color image display

True color image

Indexed color images

Pseudo color images

Color image enhancement


Light is part of the EM wave


Human Perception of Color

Retina contains receptors

Cones

Day vision, can perceive color tone

Red, green, and blue cones

Rods

Night vision, perceive brightness only

Color sensation

Luminance (brightness)

Chrominance

Hue (color tone)

Saturation (color purity)

Fromhttp://www.macula.org/anatomy

/retinaframe.html


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Frequency Responses of Cones

ybgridaCC ii ,,,,)()( == Fall 2004 EL512 Image Processing Lecture 8, Page 6

Illuminating and Reflecting Light

Illuminating sources: emit light (e.g. the sun, light bulb, TV monitors)

perceived color depends on the emittedfrequency

Reflecting sources: reflect an incoming light (e.g. the color dye,

matte surface, cloth)

perceived color depends on reflected

frequency (=emitted frequency - absorbedfrequency


Tri-chromatic Color Mixing

Tri-chromatic color mixing theory Any color can be obtained by mixing three primary colors with a

right proportion

Primary colors for illuminating sources:

Red, Green, Blue (RGB) Color monitor works by exciting red, green, blue phosphors

using separate electronic guns

follows additive rule: R+G+B=White

Primary colors for reflecting sources (also known assecondary colors): Cyan, Magenta, Yellow (CMY)

Color printer works by using cyan, magenta, yellow and black(CMYK) dyes

follows subtractive rule: R+G+B=Black


RGB vs CMY

Magenta = Red + BlueCyan = Blue + GreenYellow = Green + Red

Magenta = White - GreenCyan = White - RedYellow = White - Blue


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Red

Green Blue

A Color Image


Tristimuls Values

Tristimulus valueThe amounts of red, green, and blue needed

to form any particular color are called thetristimulus values, denoted by X, Y, and Z.

Trichromatic coefficients

Only two chromaticity coefficients are

necessary to specify the chrominance of alight.

.,,ZYX

Zz

ZYX

Yy

ZYX

Xx

++=

++=

++=

1=++ zyx


CIE Chromaticity Diagram

CIE (CommissionInternationale deLEclairage,InternationalCommission onIllumination ) systemof color specification

x axis: redy axis: green

The point marked with GREEN

x: 25%, y: 62%, z: 13%.


Color Models

Specify three primary or secondary colorsRed, Green, Blue.

Cyan, Magenta, Yellow.

Specify the luminance and chrominanceHSB or HSI (Hue, saturation, and brightness or

intensity)

YIQ (used in NTSC color TV)

YCbCr (used in digital color TV)

Amplitude specification:8 bits per color component, or 24 bits per pixel

Total of 16 million colorsA 1kx1k true RGB color requires 3 MB memory


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RGB Color Model

RGB 24-bit color cube


CMY and CMYK Color Models

Conversion between RGB and CMY

Equal amounts of Cyan, Magenta, andYellow produce black. In practice, thisproduce muddy-looking black. To producetrue black, a fourth color, black is added,which is CMYK color model.

.

1

11

,

1

11

=

=

Y

MC

B

GR

B

GR

Y

MC


HSI Color Model

Hue represents dominant color asperceived by an observer. It is an attributeassociated with the dominant wavelength.

Saturation refers to the relative purity orthe amount of white light mixed with a hue.The pure spectrum colors are fullysaturated. Pink and lavender are lesssaturated.

Intensity reflects the brightness.


The HSI Color Model


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Conversion Between RGB and HSI

Converting color from RGB to HSI

Converting color from HSI to RGB

[ ]

[ ]

][3

1

)],,[min()(

31

))(()(

)()(

2

1

cos,3602

12

1

BGRI

BGRBGR

S

BGBRGR

BRGR

withGBif

GBifH

++=

++=

+

+

=

>

=

)(1

)60cos(

cos1

)1(

BRG

H

HSIR

SIB

+=

+=

=

RG sector (0H


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Demo Using Photoshop


Color Image Display and Printing

Display:

Need three light sources projecting red, green, blue

components respectively at every pixelAnalog display: raster scan

Digital display: directly projecting at all pixel locations

Printing:

Need three (or more) color dyes (Cyan, Magenta,Yellow, and Black)

Analog printing

Digital printing

Out of gamut color


Color Image Display

RedLUT

RedGun

GreenLUT

RedGun

Blue

LUT

Red

Gun

Red signal

Green signal

Blue signal

0

1

255

Input Output


Color Gamut

Each color model has different color range (or gamut). RGB model has a larger gamut than CMY.

Therefore, some color that appears on a screen may not be printable and is replaced by the closestcolor in the CMY gamut.


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Demo with Photoshop

Using photoshop to show how out of gamut color

(view->Gamut warning).


Gamma Correction

The intensity to voltage response curve ofthe computer monitor is not linear.

Gamma correction

Sample Input to Monitor

Output from Monitor

Graph of Input

Graph of Output L=V2.5

Sample Input to Monitor

Monitor Output

Gamma corrected Input

Graph of Input

Graph of Output

Graph of Correction L=L1/2.5


Color Quantization

Select a set of colors that are most frequently used in animage, save them in a look-up table (also known as colormap or color palette)

Any color is quantized to one of the indexed colors Only needs to save the index as the image pixel value

and in the display buffer

Typically: k=8, m=8 (selecting 256 out of 16 million)

Input index (k bits) Red color (m bits) Green color (m bits) Blue color (m bits)

Index 1

.

Index 2^k


Uniform vs. Adaptive Quantization

Uniform (scalar quantization)

Quantize each color component uniformly

E.g. 24 bit-> 8 bit can be realized by using 3 bits (8 levels) forred, 3 bits (8 levels) for green, 2 bits (4 levels) for blue

Do not produce good result

Adaptive (vector quantization)

Treating each color (a 3-D vector) as one entity. Findsthe Ncolors (vectors) that appear most often in agiven image, save them in the color palette

(codebook). Replace the color at each pixel by the

closest color in the codebook

The codebook (I.e. color palette) varies from image toimage -> adaptive


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Illustration of the Vector Quantization

x

y

x

y

Uniform Quantization Vector Quantization

Codebook size: 25


24 bits -> 8 bits

Adaptive (non-uniform) quantization(vector quantization)

Uniform quantization(3 bits for R,G, 2 bits for B)

Example of Color Image Quantization


Web Colors: 216 Safe RGB Colors

These colors are those that can be rendered consistently by different computer systems. They are obtainedby quantizing the R,G,B component independently using uniform quanitization. Each component is

quantized to 6 possible values: 0(0x00), 51(0x33), 102(0x66), 153(0x99), 204(0xCC), 255(0xFF).


Color Dithering

Color quantization may cause contour effect when thenumber of colors is not sufficient

Dithering: randomly perturb the color values slightly to

break up the contour effect fixed pattern dithering diffusion dithering (the perturbed value of the next pixel depends

on the previous one)

Developed originally for rendering gray scale image using blackand white ink only

Original

value(R,G, or B)

Ditheringvalue

Dithered

value


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8 bit uniformwithout dithering

8 bit uniformwith diffusion dithering

Example of Color Dithering


Demo Using Photoshop

Show quantization results with different methods

using image->mode->index color

PaletteSystem(Windows)

256 colors


Pseudo Color Image

Why?

Human eye is more sensitive to changes in

the color hue than in brightness.

How?

Use different colors (different in hue) to

represent different image features in amonochrome image.


Pseudo Color Display

Intensity slicing: Display different gray levelsas different colors

Can be useful to visualize medical / scientific /

vegetation imagery E.g. if one is interested in features with a certain intensity

range or several intensity ranges

Frequency slicing: Decomposing an image intodifferent frequency components and represent

them using different colors.


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Intensity Slicing

Pixels with gray-scale (intensity) value in the range of (fi-1 , fi) are rendered with color Ci

f0 =0

C3

C2

C1

f2f1 f3 f4

C4

Gray level

Color


Example


Another Example


Pseudo Color Display of Multiple Images

Display multi-sensor images as a single colorimage

Multi-sensor images: e.g. multi-spectral images by

satellite


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An Example


Example


Color Image Enhancement

Enhance each primary color componentindependently using the techniques formonochrome images

Will change the color hue of the originalimage

Convert the tri-stimulus representation intoa luminance / chrominance representation,and enhance the contrast of the luminancecomponent only.

Use HSI representation, where I truly reflectsthe luminance information.


Example of Color Image Enhancement


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Homework

1. (Computer Assignment) Write a program which first performs highpass filtering(you can use matlab func conv2 for this part) of an input gray scale image using thefollowing filter:

Scale the filtered image to range between 0 and 255. Then displays the filteredimage using 3 pseudo colors, using the following color transformation: Color Red forvalues 0-80, Color Green for values 81-160, color blue for values 161-255. In matlab,you can use the function colormapto change the colormap and use imshowtodisplay an image using a specified colormap. Comment on the visual effect, e.g.each color represents what attributes of the image?

2. (Computer Assignment) Choose a 24 bit RGB color image, perform the followingoperations: 1) convert it to YIQ format, save the resulting images in three separatefiles (for Y, I and Q components respectively), each with 8 bits/pixel; 2) performhistogram equalization to the Y image; 3) convert the enhanced Y image and theoriginal I and Q image back to the RGB image. View the original and enhanced colorRGB images and comment on your observations.

3. (Computer Assignment) Choose a 24 bit color RGB image, quantize the R, G, and Bcomponents to 3, 3, and 2 bits, respectively, using a uniform quantizer in the range0-256. Display the original and quantized color image using the original colormapassociated with the image. Comment on the difference in color accuracy. Make sureyou use a computer that has a 24 bit color display, and the test image has goodcolor contrast.

010

141

010

4

1


Reading

Prof. Yao Wangs Lecture Notes, Chapter6.

R. Gonzalez, Digital Image Processing,Chapter 6.

A. K. Jain, Fundamentals of Digital ImageProcessing, Section 3.7 ~ 3.11, 7.7 ~ 7.8.

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