Introduction to color spaces l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 108

Introduction to Color Spaces PowerPoint PPT Presentation


  • 260 Views
  • Uploaded on
  • Presentation posted in: General

Introduction to Color Spaces. Author: Chik-Yau Foo E-mail: [email protected] Mobile phone: 0920-767-580 v030305. Presenter: Wei-Cheng Lin E-mail: [email protected] Mobile Phone: 0912-808-362. 10 6. 10 3. Long-wave radio. Short-wave radio. 10 0. 10 9. Microwave. 10 -3.

Download Presentation

Introduction to Color Spaces

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Introduction to color spaces l.jpg

Introduction to Color Spaces

Author: Chik-Yau Foo

E-mail: [email protected]

Mobile phone: 0920-767-580

v030305

Presenter: Wei-Cheng Lin

E-mail: [email protected]

Mobile Phone: 0912-808-362


The em spectrum l.jpg

106

103

Long-wave radio

Short-wave radio

100

109

Microwave

10-3

1012

TV

Infrared

10-6

1015

Visible spectrum

Ultraviolet

10-9

1018

X-rays

10-12

1021

Gamma rays

Cosmic rays

The EM Spectrum

  • Only a small part of the EM* spectrum is visible to us.

    • This part is known as the visible spectrum.

    • Wavelength in the region of 380 nm to 750 nm.

Frequency (Hz)

Wavelength (m)

*Electro-Magnetic


Light and the human eye l.jpg

Light and the Human Eye

  • When we focus on an image, light from the image enters the eye through the cornea and the pupil.

  • The light is focused by the lens onto the retina.

Fovea

Lens

Retina

Pupil

Optic

nerve

Cornea

Iris


Rods and cones l.jpg

Rods and Cones

  • When light reaches the retina, one of two kinds of light sensitive cells are activated.

  • These cells, called rods and cones, translate the image into electrical signals.

  • The electrical signals are transmitted through the optical nerve, and to the brain, where we will perceive the image.

Rod

Cone

Retina

light


Rods twilight vision l.jpg

Relative neural response of rods as a function of light wavelength.

1.00

0.75

0.50

Relative response

0.25

0.00

400

500

600

700

Wavelength (nm)

Rods: Twilight Vision

  • 130 million rod cells per eye.

  • 1000 times more sensitive to light than cone cells.

  • Most to green light (about 550-555 nm), but with a broad range of response throughout the visible spectrum.

  • Produces relatively blurred images, and in shades of gray.

  • Pure rod vision is also called twilight vision.


Cones color vision l.jpg

Spectral absorption of light by the three cone types

1.00

M

S

L

0.75

0.50

Relative absorbtion

0.25

0.00

400

500

600

700

Wavelength (nm)

Cones: Color Vision

  • 7 million cone cells per eye.

  • Three types of cones* (S, M, L), each "tuned" to different maximum responses at:-

    • S : 430 nm (blue) (2%)

    • M: 535 nm (green) (33%)

    • L : 590 nm (red) (65%)

  • Produces sharp, color images.

  • Pure cone vision is called photopic or color vision.

*S = Short wavelength cone

M = Medium wavelength cone

L = Long wavelength cone


Photopic vs twilight vision l.jpg

Rod vision

Cone vision

  • This is because rods are distributed all over the retina, while cones are concentrated in the fovea.

Rod vision

Cone vision

130 million rods

7 million cones

Photopic vs Twilight Vision

  • There are about 20x more rods than cones in the eyes, but rod vision is poorer than cone vision.


Eye color sensitivity l.jpg

Spectral absorption of light by the three cone types

Effective sensitivity of cones (log plot)

1.00

M

S

L

0.75

0.50

Relative absorbtion

0.25

1.00

L

M

0.00

0.1

S

400

400

500

500

600

600

700

700

Wavelength (nm)

Wavelength (nm)

0.01

Relative sensitivity

S, M, and L cone distribution in the fovea

0.001

0.0001

Eye Color Sensitivity

  • Although cone response is similar for the L, M, and S cones, the number of the different types of cones vary.

  • L:M:S = 40:20:1

  • Cone responses typically overlap for any given stimulus, especially for the M-L cones.

  • The human eye is most sensitive to green light.


Theory of trichromatic vision l.jpg

r

g

b

Tristimulus values

Theory of Trichromatic Vision

  • The principle that the color you see depends on signals from the three types of cones (L, M, S).

  • The principle that visible color can be mapped in terms of the three colors (R, G, B) is called trichromacy.

  • The three numbers used to represent the different intensities of red, green, and blue needed are called tristimulus values.

=


Seeing colors l.jpg

Illumination

source

x

  • Illumination source

Object

reflectance

factor

  • Object reflectance

x

  • Observer response

Observer

spectral

sensitivity

  • The product of these three factors will produce the sensation of color.

=

r

g

b

Observer

response

Tristimulus values

(Viewer response)

Seeing Colors

  • The colors we perceive depends on:-


Additive colors l.jpg

Additive Colors

  • Start with Black – absence of any colors. The more colors added, the brighter it gets.

  • Color formation by the addition of Red, Green, and Blue, the three primary colors

  • Examples of additive color usage:-

    • Human eye

    • Lighting

    • Color monitors

    • Color video cameras

Additive color wheel


Subtractive colors l.jpg

Subtractive Colors

  • Starts with a white background (usually paper).

  • Use Cyan, Magenta, and/or Yellow dyes to subtract from light reflected by paper, to produce all colors.

  • Examples of Subtractive color use:-

    • Color printers

    • Paints

Subtractive color wheel


Using subtractive colors on film l.jpg

W

M

B

R

K

Y

C

G

Using Subtractive Colors on Film

  • Color absorbing pigments are layered on each other.

  • As white light passes through each layer, different wavelengths are absorbed.

  • The resulting color is produced by subtracting unwanted colors from white.

White light

Green

Red

Blue

Black

White

Pigment layers

Cyan

Yellow

Magenta

Yellow

Magenta

Cyan

Black

Reflecting layer (white paper)


Color matching experiment l.jpg

Primary

Mixture

Test Light

Tristimulus values

Color Matching Experiment

  • Observer views a split screen of pure white (100% reflectance).

  • On one half, a test lamp casts a pure spectral color on the screen.

  • On the other, three lamps emitting variable amounts of red, green, and blue light are adjusted to match the color of the test light.

  • The amounts of red, green and blue light used to match the pure colors were recorded when an identical match was obtained.

  • The RGB tristimulus values for each distinct color was obtained this way.

Color matching experimental setup


Metamerism l.jpg

9

Relative power

0

380

480

580

680

780

Wavelength (nm)

The dashed line represents daylight reflected from sunflower, while the solid line represents the light emitted from the color monitor adjusted to match the color of the sunflower.

Metamerism

  • Spectrally different lights that simulate cones identically appear identical.

  • Such colors are called color metamers.

  • This phenomena is called metamerism.

  • Almost all the colors that we see on computer monitors are metamers.


The mechanics of metamerism l.jpg

9

9

9

Relative power

Relative power

Relative power

0

0

0

380

380

380

480

480

480

580

580

580

680

680

680

780

780

780

Wavelength (nm)

Wavelength (nm)

Wavelength (nm)

The Mechanics of Metamerism

  • Under trichromacy, any color stimulus can be matched by a mixture of three primary stimuli.

  • Metamers are colors having the same tristimulus values R, G, and B; they will match color stimulus C and will appear to be the same color.

The two metamers look the same because they have similar tristimulusvalues.


Gamut l.jpg

Human vision gamut

Photographic film gamut

0.8

0.6

y

0.4

Monitor gamut

0.2

0

0

0.2

0.4

0.6

0.8

x

Gamut

  • A gamut is the range of colors that a device can render, or detect.

  • The larger the gamut, the more colors can be rendered or detected.

  • A large gamut implies a large color space.


Color spaces l.jpg

Color Spaces

  • A Color Space is a method by which colors are specified, created, and visualized.

  • Colors are usually specified by using three attributes, or coordinates, which represent its position within a specific color space.

  • These coordinates do not tell us what the color looks like, only where it is located within a particular color space.

  • Color models are 3D coordinate systems, and a subspace within that system, where each color is represented by a single point.


Color spaces19 l.jpg

Color Spaces

  • Color Spaces are often geared towards specific applications or hardware.

  • Several types:-

    • HSI (Hue, Saturation, Intensity) based

    • RGB (Red, Green, Blue) based

    • CMY(K) (Cyan, Magenta, Yellow, Black) based

    • CIE based

    • Luminance - Chrominance based

CIE: International Commission on Illumination


Slide20 l.jpg

Cyan

(0,1,1)

Blue

(0,0,1)

Magenta

(1,0,1)

White

(1,1,1)

Green

(0,1,0)

Black

(0,0,0)

Red

(1,0,0)

Yellow

(1,1,0)

RGB Color Space

RGB*

  • One of the simplest color models. Cartesian coordinates for each color; an axis is each assigned to the three primary colors red (R), green (G), and blue (B).

  • Corresponds to the principles of additive colors.

  • Other colors are represented as an additive mix of R, G, and B.

  • Ideal for use in computers.

*Red, Green, and Blue


Rgb image data l.jpg

Full Color Image

Red Channel

Green Channel

Blue Channel

RGB Image Data


Cmy k l.jpg

White

Magenta

Red

Blue

Black

Yellow

Cyan

Green

CMY(K)*

  • Main color model used in the printing industry. Related to RGB.

  • Corresponds to the principle of subtractive colors, using the three secondary colors Cyan, Magenta, and Yellow.

  • Theoretically, a uniform mix of cyan, magenta, and yellow produces black (center of picture). In practice, the result is usually a dirty brown-gray tone. So black is often used as a fourth color.

Producing other colors from subtractive colors.

*Cyan, Magenta, Yellow, (and blacK)


Cmy image data l.jpg

Cyan Image (1-R)

Full Color Image

Magenta Image (1-G)

Yellow Image (1-B)

CMY Image Data


Cmy rbg transformation l.jpg

CMY – RBG Transformation

  • The following matrices will perform transformations between RGB and CMY color spaces.

  • Note that:-

    • R = Red

    • G = Green

    • B = Blue

    • C = Cyan

    • M = Magenta

    • Y = Yellow

    • All values for R, G, B

      and C, M, Y must first

      be normalized.


Cmy cmyk transformations l.jpg

CMY – CMYK Transformations

  • The following matrices will perform transformations between CMY and CMYK color spaces.

  • Note that:-

    • C = Cyan

    • M = Magenta

    • Y= Yellow

    • K = blacK

    • All values for R, G, B

      and C, M, Y, K must first

      be normalized.


Rgb cmyk transformations l.jpg

RGB – CMYK Transformations

  • The following matrices perform transformations between RGB and CMYK color spaces.

  • Note that:-

    • R = Red

    • G = Green

    • B = Blue

    • C = Cyan

    • M = Magenta

    • Y = Yellow

    • All values for R, G, B

      and C, M, Y must first

      be normalized.


Rgb gray scale transformations l.jpg

RGB – Gray Scale Transformations

  • The luminancy component, Y, of each color is summed to create the gray scale value.

  • ITU-R Rec. 601-1* Gray scale:

    Y = 0.299R + 0.587G + 0.114B

  • ITU-R Rec. 709 D65 Gray scale

    Y = 0.2126R + 0.7152G + 0.0722B

  • ITU standard D65 Gray scale (Very close to Rec 709!)

    Y = 0.222R + 0.707G + 0.071B

*601-1: Based on an old television (NTSC: National Television System Committee) standard

709 : Based on High Definition TV colorimetry (Contemporary CRT phosphors)

ITU : International Telecommunication Union


Rgb and cmyk deficiencies l.jpg

Photographic film gamut

0.8

6 color

CMY printer

gamut

0.6

y

0.4

0.2

Monitor RGB gamut

0

0

0.2

0.4

0.6

0.8

x

RGB and CMYK Deficiencies

  • RGB and CMY color models limited to brightest available primaries (R, G, and B) and secondaries (CYM).

  • Not intuitive. We think of light in terms of color, intensity of color, and brightness.

    • Colors changed by changing R, G, B ratios.

    • Brightness changed by changing R, G, and B, while maintaining their ratios.

    • Intensity changed by projecting RGB vector toward largest valued primary color (R, G, or B).

Hexachrome: Cyan, Magenta, Yellow, Black, Orange, Green


Hsi hsl hsv l.jpg

HSI / HSL / HSV*

  • Very similar to the way human visions see color.

  • Works well for natural illumination, where hue changes with brightness.

  • Used in machine color vision to identify the color of different objects.

  • Image processing applications like histogram operations, intensity transformations, and convolutions operate on only an image's intensity and are performed much easier on an image in the HSI color space.

*H=Hue, S = Saturation, I (Intensity) = B (Brightness), L = Lightness, V = Value


Hsi color space l.jpg

Blue 240º

Red 0º

Green 120º

  • Saturation

    • Degree to which hue differs from neutral gray.

    • 100% = Fully saturated, high contrast between other colors.

    • 0% = Shade of gray, low contrast.

    • Measured radially from intensity axis.

RGB cube viewed from gray-scale axis, and rotated 30°

HSI Color Wheel

RGB cube viewed from

gray-scale axis

RGB Color Space

Saturation

0%

100%

HSI Color Space

  • Hue

    • What we describe as the color of the object.

    • Hues based on RGB color space.

    • The hue of a color is defined by its counterclockwise angle from Red (0°); e.g. Green = 120 °, Blue = 240 °.


Hsi color space31 l.jpg

100%

Intensity

Hue

0%

100%

Saturation

0%

HSI Color Space

  • Intensity

    • Brightness of each Hue, defined by its height along the vertical axis.

    • Max saturation at 50% Intensity.

    • As Intensity increases or decreases from 50%, Saturation decreases.

    • Mimics the eye response in nature; As things become brighter they look more pastel until they become washed out.

    • Pure white at 100% Intensity. Hue and Saturation undefined.

    • Pure black at 0% Intensity. Hue and Saturation undefined.


Hsi image data l.jpg

Hue Channel

Saturation Channel

Intensity Channel

Full Image

HSI Image Data


Hsi rgb l.jpg

  • Hue

  • where

  • Saturation

  • Intensity

HSI - RGB

  • For a given RGB color of (R, G, B), the same color in the HSI Model is C(x,y) = (H, S, I), where


Rgb to hsi example l.jpg

Blue

(0,0,255)

Green

(0,255,0)

Red

(255,0,0)

Blue

240º

Red

Green

120º

RGB to HSI Example

  • Consider the RGB color defined by (215, 97,198)

    R = 215, G = 97, B = 198

Therefore, HSI coordinates = (308.64°, 0.843, 0.67)


Hsi to rbg l.jpg

For 240º  H  360 º

Blue

240º

Red

For 120º  H  240 º

Green

120º

For 0º  H  120 º

HSI to RBG

  • Dependent on which sector H lies in.


Hsv color space l.jpg

100%

Value

Hue

0%

100%

Saturation

0%

HSV Color Space

  • Hue and Saturation similar to that of HSI color model.

  • V: Value; defined as the height along the central vertical axis.

  • Like Intensity in HSI, color intensity increases as Value increases.

HSV: Hue, Saturation, and Value


Hsv color space37 l.jpg

Intensity

Value

Smax at V100

Smax at I50

HSV Color Space

  • Hue and Saturation similar to that of HSI color model.

  • V: Value; defined as the height along the central vertical axis.

  • Like Intensity in HSI, color intensity increases as Value increases.

  • As Value increases, hues become more saturated. Hues do not progress through the pastels to white, just as fluorescent images never change colors even though its intensity may increase. HSV is good for working with fluorescent colors.

HSV: Hue, Saturation, and Value


Intensity operations in hsi l.jpg

Original Image

Hue

Saturation

Intensity

Intensity Operations in HSI

  • To change the individual color of any region in the RGB image, change the value of the corresponding region in the Hue image.

  • Then convert the new H image with the original S and I images to get the transformed RGB image.

  • Saturation and Intensity components can likewise be manipulated.


Disadvantages of hsi color model l.jpg

Disadvantages of HSI Color Model

There are many disadvantages to the HS color model. For example:

  • Cannot perform addition of colors expressed in polar coordinates. Transformations are very difficult because Hue is expressed as an angle.

  • For color machine vision, the hues of low-saturation may be difficult to determine accurately. Systems which must be able to differentiate all colors, saturated and unsaturated, will have significant problems using the HSI representation.

  • When saturation is zero, hue is undefined.

  • Transforming between HSI and RGB is complicated.


1931 cie standard observer r g b l.jpg

0.4

g

r

0.3

0.2

Tristimulus values

b

0.1

0.0

-0.1

380

480

580

680

780

Wavelength (nm)

1931 CIE* Standard Observer(r, g, b)

  • The following color matching functions were obtained.

  • There were problems with the r, g, b color matching functions.

  • Negative values meant that the color had to be added to the test light before the two halves could be balanced.

Color-matching functions for 1931 Standard Observer, the average of 17 color-normal observers having matched each wavelength of the equal energy spectrum with primaries of 435.8nm, 546.1 nm, and 700 nm, on a bipartite 2° field, surrounded by darkness.

*Commission Internationale de L’Éclairage


1931 cie standard observer x y z l.jpg

2.0

z

1.5

x

y

Tristimulus values

1.0

0.5

1931 standard observer (2° observer).

0.0

380

480

580

680

780

Wavelength (nm)

1931 CIE Standard Observer(x, y, z)

  • CIE adopted another set of primary stimuli, designated as X, Y, and Z.

  • Special properties of X, Y, Z:-

    • Imaginary (non-physical) primary.

    • All luminance information is contributed by Y.

    • Linearly related to R, G, B.

    • Non-negative values for all tristimulus values.


Cie 1931 xy chromaticity diagram l.jpg

CIE 1931 xy Chromaticity Diagram

2D projection of 3D CIE XYZ color space onto X+Y+Z=1 plane.

x and y calculated as follows:-

The chromaticity of a color is determined by (x,y).


Cie 1931 xy chromaticity diagram43 l.jpg

(0.5, 0.4)

CIE 1931 xy Chromaticity Diagram

  • For color C, where

    C 0.5 X + 0.4 Y + 0.1 Z

  • Color C is represented as (0.5, 0.4) on the Chromaticity diagram.


Cie 1931 xyy chromaticity diagram l.jpg

CIE 1931 xyY Chromaticity Diagram

  • Each point on xy corresponds to many points in the original 3D CIE XYZ space.

  • Color is usually described by xyY coordinates, where Y is the luminance, or lightness component of color.

  • Y starts at 0 from the white spot (D65) on the xy plane, and extends perpendicularly to 100.

  • As the Y increases, the colors become lighter, and the range of colors, or gamut, decreases.


Cie xyz d65 to srgb l.jpg

CIE XYZD65 to sRGB*

  • The following transformations allow transformations between CIE XYZD65 and the sRGB color models.

*sRGB = Standard RGB, the standard for Internet use.


Cie xyz rec 609 1 rgb l.jpg

CIE XYZRec. 609-1 - RGB

  • The following are the transformations needed to convert between CIE XYZRec.609-1 and RGB.


Cie xyz rgb rec 709 l.jpg

CIE XYZ - RGBRec. 709

  • Use the following matrices to transform between CIE XYZ and Rec. 709 RGB (with its D65 white).


Xyz d65 xyz d50 transformations l.jpg

XYZD65 - XYZD50 Transformations

  • If the illuminant is changed from D50 to D65, the observed color will also change.

  • The following matrices enable transformations between XYZD65 and XYZD50.


Inadequacies in the 1931 xy chromaticity diagram l.jpg

Inadequacies in the 1931 xy Chromaticity Diagram

  • Each line in the diagram represents a color difference of equal proportion.

  • The lines vary in length, sometimes greatly, depending on what part of the diagram they're in.

  • The differences in line length indicates the amount of distortion between parts of the diagram.


Cie 1960 u v chromaticity diagram l.jpg

CIE 1960 u,vChromaticity Diagram

  • To correct for the deformities in the 1931 xy diagram, a number of uniform chromaticity scale (UCS) diagrams were proposed.

  • The following formula transforms the XYZ values or x,y coordinates to a set of u,v values, which present a visually more accurate 2D model.


Cie 1976 u v chromaticity diagram l.jpg

CIE 1976 u', v' Chromaticity Diagram

  • But the 1960 uv diagram was still unsatisfactory.

  • In 1975, CIE modified the u,v diagram and by supplying new (u',v') values. This was done by multiplying the v values by 1.5. Thus in the new diagram u' = u and v' = 1.5v.

  • The following formulas allow transformation between u’v’ and xy coordinates.


Cie 1976 u v chromaticity diagram52 l.jpg

CIE 1976 u', v' Chromaticity Diagram

  • Each line in the diagram represents a color difference of equal proportion.

  • While the representation is not perfect (it can never be), the u',v' diagram offers a much better visual uniformity than the xy diagram.


Cie l u v color space cieluv l.jpg

CIE L*u*v* Color Space/ CIELUV

  • Replaces uniform lightness scale Y with L*, an visually linear scale.

  • Equations are as follows:-

    where un’ and vn’ refer the the reference white light or light source.


Cie l a b color space cielab l.jpg

CIE L*a*b* Color Space / CIELAB

  • Second of two systems adopted by CIE in 1976 as models that better showed uniform color spacing in their values.

  • Based on the earlier (1942) color opposition systemby Richard Hunter called L, a, b.

  • Very important for desktop color.

  • Basic color model in Adobe PostScript (level 2 and level 3)

  • Used for color management as the device independent model of the ICC* device profiles.

CIE L*a*b* color axes

*International Color Consortium


Cie l a b cont d l.jpg

100

L*

-a

+b

-b

+a

0

CIE L*a*b* (cont’d)

  • Central vertical axis : Lightness (L*), runs from 0 (black) to 100 (white).

  • a-a' axis: +a values indicate amounts of red, -a values indicate amounts of green.

  • b-b' axis, +b indicates amounts of yellow; -b values indicates amounts of blue. For both axes, zero is neutral gray.

  • Only values for two color axes (a*, b*) and the lightness or grayscale axis (L*) are required to specify a color.

  • CIELAB Color difference, E*ab, is between two points is given by:

(L1*, a1*, b1*)

(L2*, a2*, b2*)

CIE L*a*b* color axes


Cielab image data l.jpg

Full Color Image

L data

L-a channel

L-b channel

CIELAB Image Data


Xyz to cielab l.jpg

XYZ to CIELAB

  • Given Xn, Yn, and Zn, which are the tristimulus values for the reference white, for a point X, Y, Z:-


Cielab to xyz l.jpg

CIELAB to XYZ

  • Reverse transformation to XYZ, given L*a*b* values.

    For


Cie l c h lch l.jpg

100%

L* (Lightness)

H (Hue)

0%

100%

C* (Chroma)

0%

CIE L*C*h* (LCh)

  • Often referred to simply as LCh.

  • Same system is the same as the CIELab color space, except that it describes the location of a color in space by use of polar coordinates rather than rectangular coordinates.

  • L* is a measure of the lightness of a sample, ranging from 0 (black) to 100 (white).

  • C* is a measure of chroma (saturation), and represents distance from the neutral axis.

  • h is a measure of hue and is represented as an angle ranging from 0° to 360.


Y u v 1 ebu 2 color space l.jpg

Y’U’V’1 (EBU2) Color Space

  • Red:xR = 0.630yR = 0.340

  • Green:xG = 0.310yG = 0.595

  • Blue:xB = 0.155yB = 0.070

  • White xW= 0.312713yW = 0.329016

  • Standard color space used for analogue television transmissions in European TVs (PAL3 and SECAM4).

  • Y is the luminance (or luma) or black and white component

  • U and V represent the color differences: U = B - Y; V = R - Y

  • U represents the Blue - Yellow axis; V, the Red - Green axis.

  • Gamma for PAL is assumed to be 2.8

1Y = Luminance, U and V are chrominance components

2 European Broadcasting Union

3Phase Alternation Line video standard for Europe; U = 0.492(B-Y); V = 0.877(R-Y)

4 Sequential Couleur avec Mémoire, video standard for France, the Middle East and most of Eastern Europe


Y uv channels l.jpg

Full Color Image

Y

V (Red - Green)

U (Blue - Yellow)

Y'UV Channels


Nonlinear y u v transformations l.jpg

Nonlinear Y’U’V’Transformations

  • The following matrices allow transformations of nonlinear signals between Y’U’V’ and R’G’B.


Linear y u v transformations l.jpg

Linear Y’U’V’ Transformations

  • The following matrices allow transformations of linear signals between YUV RGB and XYZ.


Y i q 1 color space l.jpg

Y’I’Q’1 Color Space

  • Red:xR = 0.67yR = 0.33

  • Green:xG = 0.21yG = 0.71

  • Blue:xB = 0.14yB = 0.08

  • White xW= 0.310063yW = 0.316158

  • Used in NTSC2 color broadcasting in USA; compatible with black and white television, which only uses Y.

  • U and V defines colors clearly, but do not align with desired human perceptual sensitivities.

  • Y [0..1] is the luminance (or luma) component.

  • I [-0.523 .. 0.523] represents the Orange-Blue axis.

  • Q [-0.596 .. 0.596] represents the Purple-Green axis.

1Y’I’Q’ = Luminance, In-phase, and Quadrature phase.

2National Television Standards Committee video standard for North America


Yiq channels l.jpg

Full Color Image

Y Channel

I (Orange - Blue)

Q (Purple - Green)

YIQ Channels


Y i q r g b l.jpg

Y’I’Q’ – R’G’B’

  • Use the following matrices to transform linear signals between Y’I’Q’ and gamma-corrected RGB values.


Yiq yuv l.jpg

YIQ - YUV

  • YIQ - YUV transformation is simply a color rotation of 33º.

  • The following matrices can be used to transform between NTSC based YIQ and PAL based YUV.


Y c b c r color space l.jpg

Y’CbCr* Color Space

Independent of scanning standard and system primaries, therefore:-

  • No chromaticity coordinates.

  • No CIE XYZ matrices.

  • No assumptions about white point.

  • No assumptions about CRT gamma.

  • Y’ is luminance, Cb is the chromaticity component for blue, and Cr is the chromaticity component for red.

  • Very closely related to the YUV, it is a scaled and shifted YUV.

    Cb = (B - Y) / 1.772 + 0.5 Cr = (R - Y) / 1.402 + 0.5

  • Chrominance values Cb and Cr are [ 0..1 ].

  • Deals only with digital representation of R’G’B’ signals in Y’CbCr form.

  • Color format for JPEG1 and MPEG2.

1JPEG = Joint Photography Experts Group

2MPEG = Motion Pictures Experts Group


Y c b c r rgb 0 1 l.jpg

Y'CbCr - RGB[0..+1]

  • Use the following matrices to convert between YCbCr and RGB ranging from [0 .. +1]


Itu r 601 yc b c r r g b 219 l.jpg

ITU-R.601 YCbCr - R’G’B’219

  • ITU-R.601 defines 16 =<Y >= 235, and 16 =<Cb and Cr >= 240, with 128 corresponding to 0.

  • These BT.601 equations are used by many video ICs to convert between digital R’G’B’ and BT.601 YCbCr data.

  • The R’G’B’ values produced have a nominal range of 16 - 235.

ITU-R.601 = International Telecommunication Union – Radio communications Recommendation 601

RGB219 = A restricted color space used to match YUV standard transmission values


Itu r 601 yc b c r r g b 0 255 l.jpg

ITU-R.601 YCbCr - R’G’B’0-255

  • If 24 bit R’G’B’ data needs to have a range of 0-255, the following equation should be used.

  • The R’, G’, and B’ values must be saturated at the 0 and 255 values.


Yc b c r 4 4 4 l.jpg

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

YCbCr 4:4:4

  • Full resolution

  • YCbCr 4:4:4 is in uncompressed data format.

  • Each pixel has all Y, Cb and Cr values.

  • Chrominance data can be subsampled without significant degradation in image quality.

YCbCr 4:4:4


Yc b c r 4 2 2 l.jpg

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

YCbCr 4:4:4

Y

Cb Cr

Y

Y

Cb Cr

Y

YCbCr 4:2:2

YCbCr 4:2:2

  • Obtained by a 2:1 horizontal subsampling of YCbCr 4:4:4 values.

  • Often used digital cameras, and many video ICs.

  • Restore original colors by interpolating missing Cb and Cr values from the values present.


Yc b c r 4 2 0 l.jpg

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Y

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

YCbCr 4:4:4

Y

Y

Y

Y

YCbCr 4:2:0

YCbCr 4:2:0

  • YCbCr 4:2:0 obtained by a 2:1 horizontal and vertical subsampling of YCbCr 4:4:4 values.

  • YCbCr (or, often called “YUV”) values are often subsampled to 4:2:0 before JPEG compression.

  • Restore original colors by interpolating missing Cb and Cr values from available values.


Ycbcr 4 1 1 l.jpg

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

YCbCr 4:4:4

Y

Y

Cb Cr

Y

Y

YCbCr 4:1:1

YCbCr 4:1:1

  • YCbCr 4:1:1 obtained by a 4:1 horizontal subsampling of YCbCr 4:4:4 values.

  • VHS* quality color.

VHS: Video Home System


Yc b c r 4 2 2 rgb l.jpg

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Y

Cb Cr

Y

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

Y

Cb Cr

YCbCr 4:2:2

Y

Cb Cr

Y

Y

Cb Cr

Y

Interpolation of Cb and Cr values

Y

Cb Cr

Y

Y

Cb Cr

Y

YCbCr 4:4:4

YCbCr 4:2:2 - RGB

  • Convert YCbCr 4:2:2 to YCbCr 4:4:4, through interpolation.


Yc b c r 4 2 2 rgb77 l.jpg

YCbCr 4:2:2 - RGB

  • Convert YCbCr 4:2:2 to YCbCr 4:4:4, through interpolation.

  • Convert YCbCr 4:4:4 to nonlinear R’G’B’.


Yc b c r 4 2 2 rgb78 l.jpg

YCbCr 4:2:2 - RGB

  • Convert YCbCr 4:2:2 to YCbCr 4:4:4, through interpolation.

  • Convert YCbCr 4:4:4 to nonlinear R’G’B’.

  • If necessary, convert nonlinear R’G’B’ to linear RGB by removing gamma information.

For (R’, G’, B’) < 21

For (R’, G’, B’)  21


Smpte c rgb color space l.jpg

SMPTE*-C RGB Color Space

  • Red:xR = 0.630yR = 0.340

  • Green:xG = 0.310yG = 0.595

  • Blue:xB = 0.155yB = 0.070

  • White xW= 0.312713yW = 0.329016

  • Current color standard for broadcasting in America, replacing older NTSC standard.

  • Reason for standard change: original set of (YIQ) primaries being slowly changed to YUV primaries.

  • CRT gamma assumed to be 2.2 with NTSC, 2.8 with PAL.

*Society of Motion Picture and Television Engineers


Linear smpte c rgb transformations l.jpg

Linear SMPTE-C RGB Transformations

  • The following matrices allow transformations of linear signals between SMPTE-C RGB and XYZ.


Nonlinear smpte c rgb transformation l.jpg

Nonlinear SMPTE-C RGB Transformation

  • The transformation matrices for non-linear signals are the same as that of the older YIQ (NTSC) standard.


Itu bt 709 in y cbcr l.jpg

ITU.BT-709 in Y'CbCr

  • Recent standard, defined only as an interim standard for HDTV studio production.

  • Defined by the CCIR (now the ITU-R) in 1988, but is not yet recommended for use in broadcasting.

  • The primaries are the R and B from the EBU, and a G which is midway between SMPTE-C and EBU.

  • CRT gamma is assumed to be 2.2.

  • Red:xR = 0.64yR = 0.33

  • Green:xG = 0.30yG = 0.60

  • Blue:xB = 0.15yB = 0.06

  • White (D65):xW= 0.312713yW = 0.329016

ITU: International Telecommunication Union

CCIR: Comite Consultatif International des Radiocommunications


Linear xyz rec 709 rgb d65 l.jpg

Linear XYZ Rec.709 – RGBD65

  • The following matrices allow transformation between linear signals of Rec.709 XYZ values and RGBD65.


Rgb ebu rgb 709 l.jpg

RGBEBU – RGB709

  • The following matrices allow transformation between linear Rec. 709 RGB signals and EBU* RGB signals.

European Broadcasting Union


Nonlinear y c b c r 709 r g b l.jpg

Nonlinear Y’CbCr 709– R’G’B’

  • The following matrices allow transformation between nonlinear Rec.709 Y’CbCr signals and R’G’B’.

  • Scaling optimized for digital video.


Smpte 240m y p b p r hdtv l.jpg

SMPTE-240M Y’PbPr (HDTV*)

  • Red:xR = 0.67yR = 0.33

  • Green:xG = 0.21yG = 0.71

  • Blue:xB = 0.15yB = 0.06

  • White xW= 0.312713yW = 0.329016

  • This one of the developments of NTSC component coding, in which the B primary and white point were changed. With this space color, all three components Y’, Pb, and Pr are linked to luminance.

  • Standard for coding High Definition TV broadcasts in the USA.

  • The CRT gamma law is assumed to be 2.2.

*High Definition TeleVision


Rgb 240m rgb 709 l.jpg

RGB240M - RGB709

  • The following transforms between SMPTE* 240M (SMPTE RP 145 or Y'PbPr) RGB to Rec. 709 RGB.

*Society of Motion Picture and Television Engineers

240M = Recommended Standard for USA’s HDTV


Rgb 240m rgb ebu l.jpg

RGB240M - RGBEBU

  • The following transforms from SMPTE 240M (SMPTE RP 145, or YPbPr) RGB into to Rec. 709 RGB.


Linear smpte 240m xyz rgb l.jpg

Linear SMPTE-240M XYZ - RGB

  • The following matrices allow linear transformations between SMPTE-240M XYZ and RGB.


Nonlinear smpte 240m y pbpr transformations l.jpg

Nonlinear SMPTE-240M Y’PbPr Transformations

  • The following matrices allow nonlinear transformations between Y’PbPr and R’G’B’.

  • Scaling suited for component analogue video.


Xerox corporation y e s 1 l.jpg

Xerox Corporation Y’E’S’1

  • Standard proposed by Xerox Corporation.

  • YES has three components:

    • Y, or luminancy,

    • E, or chrominancy of the red-green axis, and

    • S, chrominancy of the yellow-blue axis.

  • The following examples assume a CRT gamma of 2.2.

1YES = Luminance, E = red-green chromaticity, S = blue-yellow chromaticity


Y e s to xyz d50 transformation l.jpg

Y’E’S’ to XYZD50 Transformation

  • If you start with non-linear Y’E’S’ values, apply a gamma correction to convert to linear YES values first:-

  • Next, apply the following transformation to the linear YES.


Xyz d50 to yes transformation l.jpg

XYZD50 to YES Transformation

  • First, apply the following transformation matrix to obtain linear YES from XYZD50.

  • For non-linear Y’E’S’ values, apply a gamma correction.


Yes to xyz d65 transformation l.jpg

YES to XYZD65 Transformation

  • As before, if you start with non-linear Y’E’S’ values, apply a gamma correction to convert to linear YES values first:-

  • Next, apply the following transformation to the linear YES.


Xyz d65 to yes transformation l.jpg

XYZD65 to YES Transformation

  • First, apply the following transformation matrix to obtain linear YES from XYZD50.

  • If required, apply a gamma correction to obtain Y’E’S’.


Kodak photo cd ycc yc 1 c 2 color space l.jpg

Kodak Photo CD YCC (YC1C2) Color Space

  • Based on Rec. 709 and 601-1, the YCC color space has color gamut defined by the Rec. 709 primaries and a luminance - chrominance representation of color like ITU 601-1's YCbCr.

  • YCC provides a color gamut that is greater than that which can currently be displayed, and is therefore suitable not only for both additive and subtractive (RGB and CMY(K)) reproduction.

  • Extended color gamut obtainable by the PhotoCD system is achieved by allowing both positive and negative values for each primary, allowing YCC to store more colors than current display devices, such as CRT monitors and dye-sublimation printers, can produce.


Transformations to encode kodak yc 1 c 2 data l.jpg

Transformations to Encode Kodak YC1C2 Data

  • First, apply a gamma correction:

  • Next, transform the R’G’B’ data into YC1C2 data.

  • Scaling is optimized for films.

For R709, G709, B709 0.018

For R709, G709, B709 0.018


Transformations to encode yc 1 c 2 data cont d l.jpg

Transformations to Encode YC1C2 Data (cont’d)

  • Finally, store the floating point values as 8-bit integers.

  • The unbalanced scale difference between Chroma1 and Chroma2 is designed, according to Kodak, to follow the typical distribution of colors in real scenes.


Transforming yc 1 c 2 data to 24 bit rgb l.jpg

Transforming YC1C2 Data to 24-bit RGB

  • Kodak YCC can store more information than current display devices can cope with (it allows negative RGB values), so the transforms from YCC to RGB are not simply the inverse of RGB to YCC, they depend on the target display system.

    First, recover normal Luma (Y) and Chroma (C1 and C2) data.

    Second, if the display primaries match Rec. 709 primaries in their chromaticity, then


Yc 1 c 2 rgb signal voltages l.jpg

YC1C2 – RGB Signal Voltages

  • First, recover normal Luma (Y) and Chroma (C1 and C2).

  • Then, calculate the RGB display voltages as follows;


Photoycc yc b c r l.jpg

  • Transform PhotoYCC color space into YCbCr values as follows:-

  • Transform YCbCr data into PhotoYCC color space as follows:-

PhotoYCC - YCbCr

  • As the PhotoYCC color space is larger than the YCbCr color space, the produced image may be poorer than the original.

  • The image produced may not match an image that was one encoded directly in PhotoYCC color space.


Srgb specs l.jpg

sRGB specs

CIE chromaticities for ITU-R BT.709 reference primaries and CIE standard illuminant

RedGreenBlueD65 White Point

x 0.64000.30000.1500 0.3127

y 0.33000.60000.0600 0.3290

z 0.03000.10000.7900 0.3583

sRGB Viewing Environment Summary

ConditionsRGB

Display Luminance level 80 cd/m2

Display White Point x = 0.3127, y = 0.3290 (D65)

Display model offset (R, G and B)0.0

Display input/output characteristic2.2

Reference ambient illuminance level 64 lux

Reference Ambient White Point x = 0.3457, y = 0.3585 (D50)

Reference Veiling Glare 0.2 cd m-2


Glossary of color models l.jpg

Glossary of Color Models

brightness - the human sensation by which an area exhibits more or less light.

lightness - the sensation of an area's brightness relative to a reference white in the scene.

luma - Luminance component corrected by a gamma function and often noted Y'.

chroma - the colorfulness of an area relative to the brightness of a reference white.

saturation - the colorfulness of an area relative to its brightness.

CCIR: Comite Consultatif International des Radiocommunications


Glossary of illuminants and their reference whites l.jpg

Glossary of Illuminants and Their Reference Whites


2d color spaces l.jpg

RGB Color Space

ITU Color Space

HLS Color Space

NTSC Color Space

HSV Color Space

Rec.709 Color Space

SMPTE Color Space

2D Color Spaces


References l.jpg

References

  • BARCO Introduction to Color Theory, Monitor Calibration and Color Management, http://www.barco.com/display_systems/support/colorthe/colorthe.htm

  • R. S. Berns, Principles of Color Technology (3rd Ed)., 2000

  • S. M. Boker, The Representation of Color Metrics and Mappings in Perceptual Color Space, http://kiptron.psyc.virginia.edu/steve_boker/ColorVision2/ColorVision2.html

  • D. Bourgin, Color spaces FAQ, http://www.inforamp.net/~poynton/notes/Timo/colorspace-faq, 1996,

  • R. Buckley, Xerox Corp., G. Bretta, Hewlett-Packard Laboratories, Color Imaging on the Internet, http://www.inventoland.net/imaging/cii/nip01.pdf, 2001

  • Color Representation, http://203.162.7.85/unescocourse/computervision/comp_frm.htm


References cont d l.jpg

References (cont’d)

  • A. Ford and A. Roberts, Color Space Conversions, www.inforamp.net/~poynton/PDFs/coloureq.pdf, 1998

  • Gonzales, Woods, Digital Image Processing, 2000

  • A. Kankaanpaa, Color Formats, www.physics.utu.fi/ett/kurssi/sfys3066/arto_tiivis.pdf, 2000.

  • M. Nielsen and M. Stokes, Hewlett-Packard Company, The Creation of the sRGB ICC Profile, http://www.srgb.com/c55.pdf

  • C. Poynton, Frequently Asked Questions about Color, http://www.inforamp.net/~poynton/ColorFAQ.html, 1999

  • C. Poynton, Frequently Asked Questions about Gamma, http://www.inforamp.net/~poynton/GammaFAQ.html, 1999

  • G. Starkweather, Colorspace interchange using sRGB, http://www.microsoft.com/hwdev/tech/color/sRGB.asp, 2001


The end l.jpg

The End

- Question and Answer Session -


  • Login