Color Spaces

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Color Spaces

Babol university of technologyECE Dep.

Machine Vision

Prof: M. Ezoji

Presentation: Alireza Asvadi

Fall 2012

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1. Human Color Perception

2. Linear Color Spaces 2.1 CIE XYZ 2.2 RGB 2.3 CMYK 2.4 YIQ 2.5 YUV

3. Non-linear Color Spaces 3.1 HSV 3.2 HSI (HSL,HSB) 3.3 CIE u’v’ 3.4 CIE LAB

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Different colors correspond to radiation of different wavelengths.

The simplest question is to understand which spectral energy densities produce the same response from people under simple viewing conditions.

Color Matching Experiment

Goal: find out what spectral radiances produce same response in human observers.

R. C. Gonzalez, R. E. Wood, “Digital Image Processing,”

4Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

Color matching experiment 1

Slide credit: W. Freeman


p1 p2 p3 Slide credit: W. Freeman




p1 p2 p3

The primary color amounts needed for a match









p1 p2 p3 p1 p2 p3

We say a “negative” amount of p2 was needed to make the match, because we added it to the test color’s side.

The primary color amounts needed for a match:

p1 p2 p3

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p1 p2 p3

The RGB color space cannot always produce a color equivalent to any wavelength. In order to produce these colors the red component sometimes should be negative.

One way to avoid this problem is to specify color matching functions that are everywhere positive

RGB color matching functions

D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach,”

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The CIE XYZ color space is one quite popular standard.The color matching functions were chosen to be everywhere positive.

( , ) ( , )X Yx yX Y Z X Y Z

CIE XYZ Color matching functions


15R. C. Gonzalez, R. E. Wood, “Digital Image Processing,”

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Color Gamut produced by RGB monitors

Color Gamut produced by high quality color printing device

color space's color gamut: subset of colors which can be accurately represented in a given color space .


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24-bit RGB color image: 8-bit for each color.

Able to represent:

382 Colors 16,777,216 Colors

Yellow

Magenta Cyan

RGB:

The RGB color space is a linear color space that formally uses single wavelengthPrimaries. Informally, RGB uses whatever phosphors a monitor has as primaries.

p1 = 645.2 nmp2 = 525.3 nmp3 = 444.4 nm

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uint8

double

In MATLAB the values of RGB are assumed to be in the range of [0,1] (double)or in the range of [0-255] (uint8)or in the range of [0-65535] (uint16)

19Red Green Blue

RGB Image

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00.1

0.20.3

0.40.5

0.60.7

0.80.9

1

00.1

0.20.3

0.40.5

0.60.7

0.80.9

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Blue

RedGreen RedGreen

Blue

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CMY – CMYK:

The name CMYK refers:CyanMagentaYellowBlack

Primaries:Cyan, magenta, yellowSecondaries :Red, green, blue

BGR

YMC

111 Red -> complements <- Cyan

Green -> complements <- MagentaBlue -> complements <- Yellow

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pigments remove color from incident light, which is reflected from paper.

Thus, red ink is really a dye that absorbs green and blue light—incident red light passes through this dye and is reflected from the paper. In this case, mixing is subtractive.

Additive Color: Monitors combined RedGreen, and Blue light to Produce “White”

The mixing of “light”

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Subtractive Color:The mixing of “pigment”Pigments absorb light

Theoretically black is not neededBut when full-saturation cyan, magenta, and yellow inks are mixed equally on paper result is usually a dark brown, rather than black.

Red + Green = YellowRed + Blue = MagentaGreen + Blue = Cyan

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RGB Image

Cyan Magenta Yellow

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YIQ Color Space:

Y : luminance, brightnessI, Q: chrominance (color information)

By separating the intensity from the color information makes the YIQ color space very attractive to TV broadcasting, because it helps maintain compatibility with monochrome TV standards.

The YIQ model also takes advantage of the fact that the human eye is more sensitive to changes in luminance than changes to hue or saturation.

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RGB Image

Y Image

I Image

Q Image

YIQ: MATLAB Command

yiq_image = rgb2ntsc(rgb_image);

rgb_image= ntsc2rgb(yiq_image);

BGR

QIY

312.0523.0211.0322.0274.0596.0

114.0587.0299.0

QIY

BGR

703.1106.1000.1647.0272.0000.1

621.0956.0000.1

Ref: Wikipedia – YIQ

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The YUV color space is used by the PAL and SECAM color television systems in many countries.

The luminance value Y and two color differences U, V can be expressed with the following formula:

U=(B-Y)/2.03 = 0.493(B-Y)V=(R-Y)/1.14=0. 877(R-Y)

The YUV color space is very similar to the YIQ color space and both were proposed to be used with the NTSC standard, but because the YIQ color space needs a lower bandwidth that YUV, the YIQ color space was chosen.

YUV color space :

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Color space

Color mixing

Primary parameters

Used for Pros and cons

RGB Additive Red,Green, Blue

Easy but wasting bandwidth

CMYK Subtractive Cyan, Magenta, Yellow, Black

Printer Works in pigment mixing

YCbCrYPbPr

additive Y(luminance), Cb(blue chroma), Cr(red chroma)

Video encoding, digital camera

Bandwidth efficient

YUV additive Y(luminance),U(blue chroma), V(red chroma)

Video encoding for NTSC, PAL,

SECAM

Bandwidth efficient

YIQ additive Y(luminance),I(rotated from U),Q(rotated from V)

Video encoding for NTSC

Bandwidth efficient

Ref: color spaces slides. Presenter: Cheng-Jin Kuo Advisor: Jian-Jiun Ding, Ph. D. Professor Digital Image & Signal Processing Lab Graduate Institute of Communication Engineering National Taiwan University, Taipei, Taiwan, ROC

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HSV:

Hue: true color attributeThe first thing we usually notice about a color is its hue.The range of H is represented by values from 0 to 360

Saturation: amount that the color is diluted by white. pure red high saturationlight red low saturation

Value: degree of brightness.White values have the maximum brightnessblack values have no brightness

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H: from 0 to 360Red = 0Green = 120Blue = 240Yellow = 60 Cyan = 180Magenta = 300

Ref: color spaces slides from Thomas Mitchell

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HSV RGB

VS

BGRVBGRV

},,min{},,max{

GRHV

RBHVG

BGHVR

461 THEN B IF

261 THEN IF

61 THEN IF

))1(1()1(

)1(6

6

FSVTSFVQSVPHHF

HH

H’ R G B0 V T P1 Q V P2 P V T3 P Q V4 T P V5 V P Q

All values are normalized.

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HSV: MATLAB Command

hsv_image = rgb2hsv(rgb_image);

*The elements of rgb_image can be in the range double[0 1] or uint8 [0 255]

rgb_image is an m-by-n-by-3 image array whose three planes contain the red, green, and blue components for the image. hsv_image is returned as an m-by-n-by-3 image array whose three planes contain the hue, saturation, and value components for the image.

rgb_image= hsv2rgb(hsv_image);

*The elements of both are in the range 0 to 1.

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RGB Image

Hue Image Saturation Image Value Image

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HSI (HSL or HSB):

HSI and HSV are quite similar color spaces.The difference is that in HSV space to get white color you should set Saturation to "0". But in HSI space at I=1 you get white regardless the saturation value.

HSI (HSL or HSB):HSV:

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GB if 360GB if

H

2/121

)])(()[(

)]()[(21

cosBGBRGR

BRGR

)],,[min()(

31 BGRBGR

S

)(31 BGRI

RGB HSI

HSI RGB 1200 HRG sector : )1( SIB

)60cos(

cos1HHSIR

)(3 BRIG

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HSI RGB 240120 HGB sector :

)1( SIR

)60cos(

cos1HHSIG

)(3 GRIB

120HH

360240 HBR sector :

)1( SIG

)60cos(

cos1HHSIB

)(3 BGIR

240HH

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One can determine just noticeable differences by modifying a color shown toobservers until they can only just tell it has changed in a comparison with theoriginal color. When these differences are plotted on a color space, they form theboundary of a region of colors that are indistinguishable from the original colors.

Uniform Color Spaces:

determine just noticeable differences


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This figure shows the CIE 1976 u’v’ space, which is obtained by a projective transformation of CIE x, y space. The intention is to make the MacAdam ellipses uniformly circles. This would yield a uniform color space.

CIE u’v’:


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CIE LAB obtained as a non-linear mapping of the XYZ coordinates:

CIE LAB:

Here Xn, Yn, and Zn are the X, Y , and Z coordinates of a reference white patch.The LAB space is substantially uniform.


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R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital Image Processing UsingMATLAB,” New Jersey, Prentice Hall, 2003.

R. C. Gonzalez, R. E. Wood, “Digital Image Processing,” Prentice Hall, 2nd Edition, 2002.

D. A. Forsyth, J. Ponce, “Computer Vision: A Modern Approach,” Prentice Hall,2nd Edition, 2012.

Documents

Color Spaces