Digital Image Processing Lecture # 14 Color Image Processing

Digital Image ProcessingDigital Image Processing

Lecture # 14Lecture # 14

Color Image ProcessingColor Image Processing

Fall 2012Fall 2012

CP-7008: Digital Image Processing Lecture # 14 2

Color FundamentalsColor Fundamentals



The colors that humans and most animals perceive in an object are determined by the nature of the light reflected from the object►For example, green objects reflect light with wave lengths primarily in the range of 500 – 570 nm whileabsorbing most of the energy at other wavelengths

White LightColours Absorbe

d

Green Light


► Color ModelColor Model A mathematical system for representing colorA mathematical system for representing color

► The human eye combines 3 primary colors (using the 3 The human eye combines 3 primary colors (using the 3 different types of cones) to discern all possible colors.different types of cones) to discern all possible colors.

► Colors are just different light frequenciesColors are just different light frequencies red red –– 700nm wavelength 700nm wavelength green green –– 546.1 nm wavelength 546.1 nm wavelength blue blue –– 435.8 nm wavelength 435.8 nm wavelength

► Higher frequencies are Higher frequencies are coolercooler colors colors

4




► 6 to 7 million cones in the human eye can be divided into three principal sensing categories, corresponding roughly to red, green, and blue.

65%: red 33%: green 2%: blue (blue cones are the most sensitive)





► The characteristics generally used to distinguish one color from another are brightness, hue, and saturation

brightness: the achromatic notion of intensity.

hue: dominant wavelength in a mixture of light waves, represents dominant color as perceived by an observer.

saturation: relative purity or the amount of white light mixed with its hue.



3 basic qualities are used to describe the quality of a 3 basic qualities are used to describe the quality of a chromatic light source:chromatic light source:

Radiance:Radiance: the total amount of energy that flows from the the total amount of energy that flows from the light source (measured in watts)light source (measured in watts)

Luminance:Luminance: the amount of energy an observer the amount of energy an observer perceives perceives from the light source (measured in lumens)from the light source (measured in lumens)

► Note we can have high radiance, but low luminanceNote we can have high radiance, but low luminance

Brightness:Brightness: a subjective (practically immeasurable) a subjective (practically immeasurable) notion that embodies the intensity of lightnotion that embodies the intensity of light


Primary ColorsPrimary Colors

► Primary colors of Primary colors of lightlight are are additiveadditive Primary colors are red, green, and bluePrimary colors are red, green, and blue Combining red + green + blue yields whiteCombining red + green + blue yields white

► Primary colors of Primary colors of pigmentpigment are are subtractivesubtractive Primary colors are cyan, magenta, and yellowPrimary colors are cyan, magenta, and yellow Combining cyan + magenta + yellow yields Combining cyan + magenta + yellow yields

blackblack

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RGB Color modelRGB Color model

10

Active displays, such as computer monitors and television sets, emit combinations of red, green and blue light. This is an additive color model

Source: www.mitsubishi.com


CMY Color modelCMY Color model

11

Passive displays, such as color inkjet printers, absorb light instead of emitting it. Combinations of cyan, magenta and yellow inks are used. This is a subtractive color model.

Source: www.hp.com


RGB vs CMYRGB vs CMY


RGB color cubeRGB color cube

RGB 24-bit color cube


RGB and CMY Color CubesRGB and CMY Color Cubes

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RGB ExampleRGB Example

15

Original Green Band Blue BandRed Band



16

Original No GreenNo Red No Blue




The CMY and CMYK Color ModelsThe CMY and CMYK Color Models

1

1

1

C R

M G

Y B

Equal amounts of the pigment primaries, cyan, magenta, and yellow should produce black. In practice, combining these colors for printing produces a muddy-looking black.

To produce true black, the predominant color in printing, the fourth color, black, is added, giving rise to the CMYK color model.

CP-7008: Digital Image Processing Lecture # 14 19http://en.wikipedia.org/wiki/CMYK

CMY vs. CMYK


Subtractive mixing of inksSubtractive mixing of inks

► Inks subtract light from white.Inks subtract light from white.► Linearity depends on pigment properties Linearity depends on pigment properties

inks, paints, often hugely non-linear.inks, paints, often hugely non-linear.► Inks: Cyan=White-Red, Magenta=White-Green, Yellow=White-Inks: Cyan=White-Red, Magenta=White-Green, Yellow=White-

Blue.Blue.► For a good choice of inks, For a good choice of inks, and good registrationand good registration, matching is , matching is

linear and easylinear and easy► eg. C+M+Y=White-White=Black, C+M=White-Yellow=Blueeg. C+M+Y=White-White=Black, C+M=White-Yellow=Blue► Usually require CMY and Black, because colored inks are more Usually require CMY and Black, because colored inks are more

expensive, and registration is hard (CMYK)expensive, and registration is hard (CMYK)► For good choice of inks, there is a linear transform between XYZ For good choice of inks, there is a linear transform between XYZ

and CMYand CMY


Color receptors and color deficiencyColor receptors and color deficiency

► In color normal people, In color normal people, there are three types of there are three types of color receptor, called color receptor, called conescones, which vary in their , which vary in their sensitivity to light at sensitivity to light at different wavelengths different wavelengths (shown by molecular (shown by molecular biologists).biologists).

► Deficiency by optical Deficiency by optical problems in the eye, or by problems in the eye, or by absent receptor types absent receptor types Usually a result of absent Usually a result of absent

genes.genes.

► Some people have fewer Some people have fewer than three types of than three types of receptor; most common receptor; most common deficiency is red-green deficiency is red-green color blindness in men. color blindness in men.

► Color deficiency is less Color deficiency is less common in women; red and common in women; red and green receptor genes are green receptor genes are carried on the X carried on the X chromosome, and these are chromosome, and these are the ones that typically go the ones that typically go wrong. Women need two wrong. Women need two bad X chromosomes to bad X chromosomes to have a deficiency, and this have a deficiency, and this is less likely.is less likely.


HSI Color ModelHSI Color Model

► Based on human perception of colors. Based on human perception of colors. ColorColor is is ““decoupleddecoupled”” from from intensityintensity.. HUEHUE

► A subjective measure of colorA subjective measure of color► Average human eye can perceive ~200 different colorsAverage human eye can perceive ~200 different colors

SaturationSaturation► Relative purity of the color. Mixing more Relative purity of the color. Mixing more ““whitewhite”” with a color with a color

reduces its saturation.reduces its saturation.► PinkPink has the same has the same huehue as as redred but less but less saturationsaturation

IntensityIntensity► The brightness or darkness of an objectThe brightness or darkness of an object



23

H dominant

wavelength

Spurity

% white

IIntensity

Source: http://www.cs.cornell.edu/courses/cs631/1999sp/



► HueHue is defined as an angle is defined as an angle 0 degrees is 0 degrees is REDRED 120 degrees is 120 degrees is GREENGREEN 240 degrees is 240 degrees is BLUEBLUE

► SaturationSaturation is defined as the percentage of distance from the is defined as the percentage of distance from the center of the HSI triangle to the pyramid surface.center of the HSI triangle to the pyramid surface. Values range from 0 to 1.Values range from 0 to 1.

► IntensityIntensity is denoted as the distance is denoted as the distance ““upup”” the axis from black. the axis from black. Values range from 0 to 1Values range from 0 to 1

24





26


HSI and RGBHSI and RGB

27

RGB and HSI are commonly used to specify colors in software applications.

HSI has variants such as HSL and HSB both all of which model color in the same fundamental way.


Conversion Between RGB and HSIConversion Between RGB and HSI



Image Image ““TypesTypes””(categorized by (categorized by ““colorcolor””))

►Binary ImageBinary Image has exactly two colorshas exactly two colors

►GrayscaleGrayscale has no chromatic contenthas no chromatic content

►CCoolloorr contains some pixels with colorcontains some pixels with color more than two colors existmore than two colors exist

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Color DepthColor Depth

► Describes the ability of an image to accurately reproduce Describes the ability of an image to accurately reproduce colorscolors

Given as the Given as the ““number of bits consumed by a single pixelnumber of bits consumed by a single pixel”” Otherwise known as Otherwise known as ““bits per pixelbits per pixel”” (bpp) (bpp)

► Binary images are ____ bpp?Binary images are ____ bpp?

► Grayscale images are typically ____ bpp?Grayscale images are typically ____ bpp?

► Color images are typically ____ bpp?Color images are typically ____ bpp?

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A B

C D

A: 1 bppB: 2 bppC: 5 bppD: 24 bpp


Tristimulus ValuesTristimulus Values

► Tristimulus valueTristimulus value The amounts of red, green, and blue needed to The amounts of red, green, and blue needed to

form any particular color are called the form any particular color are called the tristimulus valuestristimulus values, denoted by X, Y, and Z., denoted by X, Y, and Z.

Only two chromaticity coefficients are necessary Only two chromaticity coefficients are necessary to specify the chrominance of a light.to specify the chrominance of a light.

1 ZYX


CIE Chromacity DiagramCIE Chromacity Diagram

Specifying colors systematically can be Specifying colors systematically can be achieved using the CIE achieved using the CIE chromacity diagramchromacity diagram

On this diagram the x-axis represents the On this diagram the x-axis represents the proportion of red and the y-axis represents the proportion of red and the y-axis represents the proportion of green used proportion of green used

The proportion of blue used in a color is The proportion of blue used in a color is calculated as:calculated as:

z = 1 – (x + y)z = 1 – (x + y)


CIE Chromacity Diagram (cont…)CIE Chromacity Diagram (cont…)

Green: 62% green, Green: 62% green, 25% red and 13% 25% red and 13% blueblue

Red: 32% green, Red: 32% green, 67% red and 1% blue67% red and 1% blue



►Any color located on the boundary of the chromacity Any color located on the boundary of the chromacity chart is fully saturatedchart is fully saturated

►The point of equal energy has equal amounts of The point of equal energy has equal amounts of each color and is the CIE standard for pure whiteeach color and is the CIE standard for pure white

►Any straight line joining two points in the diagram Any straight line joining two points in the diagram defines all of the different colors that can be obtained defines all of the different colors that can be obtained by combining these two colors additivelyby combining these two colors additively

►This can be easily extended to three pointsThis can be easily extended to three points



►This means the entire This means the entire color range cannot be color range cannot be displayed based on any displayed based on any three colorsthree colors

►The triangle shows The triangle shows the typical color gamut the typical color gamut produced by RGB produced by RGB monitorsmonitors

►The strange shape is The strange shape is the gamut achieved by the gamut achieved by high quality color high quality color printersprinters


Color ModelsColor Models

► Specify three primary or secondary colorsSpecify three primary or secondary colors Red, Green, Blue.Red, Green, Blue. Cyan, Magenta, Yellow.Cyan, Magenta, Yellow.

► Specify the luminance and chrominanceSpecify the luminance and chrominance –– HSB, HSI or HSV (Hue, saturation, and brightness, HSB, HSI or HSV (Hue, saturation, and brightness,

intensity or value)intensity or value)

► Amplitude specification:Amplitude specification: 8 bits per color component, or 24 bits per pixel8 bits per color component, or 24 bits per pixel Total of 16 million colorsTotal of 16 million colors


Comparison of Different Color SpacesComparison of Different Color Spaces

Much details than other bands (can be used for processing color images)


Pseudocolor Image ProcessingPseudocolor Image Processing

► The process of assigning colors to gray values based on a specified criterion.

► Intensity Slicing

( , ) if ( , )k kf x y c f x y V




Intensity SlicingIntensity Slicing

► Pixels with gray-scale (intensity) value in the range of (Pixels with gray-scale (intensity) value in the range of (f f i-1i-1 , f , fii) ) are rendered with color are rendered with color CiCi




Pseudocolor Image ProcessingPseudocolor Image Processing

► Intensity to Color Transformation


The images are obtained from an airport X-ray scanning system.The left contains ordinary articles and the right contains the same articles as well as a block of simulated plastic explosives.


Basics of Full-Color Image ProcessingBasics of Full-Color Image Processing

Let represent an arbitrary vector in RGB color space:

At coordinates ( , ),

( , ) ( , )

( , ) ( , ) ( , )

( , ) ( , )

R

G

B

R

G

B

c

c R

c c G

c B

x y

c x y R x y

c x y c x y G x y

c x y B x y


Basics of Full-Color Image ProcessingBasics of Full-Color Image Processing


Color Image SmoothingColor Image Smoothing

( , )

Let denote the set of coordinates defining a neighborhood

centered at ( , ) in an RGB color image. The average of the

RGB component vectors in this neighborhood is

1 ( , ) ( , )

xy

xy

s t S

S

x y

c x y c s tK

( , )

( , )

( , )

1( , )

1( , )

1( , )

xy

xy

xy

s t S

s t S

s t S

R s tK

G s tK

B s tK





Color Image SharpeningColor Image Sharpening

2

2 2

2

The Laplacian of vector c is

( , )

( , ) ( , )

( , )

R x y

c x y G x y

B x y



Color Edge Detection (1)Color Edge Detection (1)

Let r, g, and b be unit vectors along the R, G, and B

axis of RGB color space, and define vectors

u r g b

and

v r g b

R G B

x x x

R G B

y y y



2 2 2

2 2 2

u u=

v v=

and

u v=

xx

yy

xy

R G Bg

x x x

R G Bg

y y y

R R G G B Bg

x y x y x y



1

The direction of maximum rate of change of c( , ) is given by

the angle

21 ( , ) tan

2

The value of the rate of change at ( , ) in the direction of ( , ),

is given by

1F ( , )=

2

xy

xx yy

x

x y

gx y

g g

x y x y

x y g

1/2

cos 2 ( , ) 2 sin 2 ( , )x yy xx yy xyg g g x y g x y




Image File FormatsImage File Formats

►To understand the advantages and To understand the advantages and disadvantages of various image formatsdisadvantages of various image formats

►CategoriesCategories One categoryOne category

►Raster Image FormatsRaster Image Formats►Vector Image FormatsVector Image Formats

Another categoryAnother category►Binary Image FormatsBinary Image Formats►ASCII Image FormatsASCII Image Formats


Raster Image FormatsRaster Image Formats► Breaks the image into a series of color Breaks the image into a series of color

dots called “pixels”dots called “pixels”► The number of bits at each pixel The number of bits at each pixel

determines the maximum number of determines the maximum number of colorscolors

1 bits= 2 (21) colors1 bits= 2 (21) colors

2 bits= 4(22) colors2 bits= 4(22) colors



16 bits= 65,536 (216) colors16 bits= 65,536 (216) colors

24 bits = 16,777,216 (224) colors !24 bits = 16,777,216 (224) colors !


Vector Image FormatsVector Image Formats

►Break the image into a set of Break the image into a set of mathematical descriptions of shapes: mathematical descriptions of shapes: curve, arc, rectangle, sphere etc.curve, arc, rectangle, sphere etc.

►Resolution-independent: scalable Resolution-independent: scalable without the problem of “pixelating” .without the problem of “pixelating” .

►Not all images are easily described in Not all images are easily described in a mathematical form.a mathematical form.

How to describe a photograph?How to describe a photograph?


ComparisonComparison

►RasterRaster

-Resolution--Resolution-dependentdependent

-Suitable for -Suitable for photographsphotographs

-smooth tones and -smooth tones and subtle detailssubtle details

-larger size-larger size

►VectorVector

-Resolution--Resolution-independent independent

-suitable for line -suitable for line drawings, CAD, drawings, CAD, LogosLogos

- Smooth curvesSmooth curves- Smaller sizeSmaller size


What are the common types of What are the common types of image formatsimage formats

► RasterRaster► GIF GIF (Graphics Interchange Format), (Graphics Interchange Format), Bitmap,Bitmap,

JPEG,TIFF, PBM JPEG,TIFF, PBM (portable Bit Map – binary), (portable Bit Map – binary), PGM (Portable Gray map – grayscale), PGM (Portable Gray map – grayscale), PPMPPM (Portable Pixel Map – color), (Portable Pixel Map – color), PNMPNM (Portable (Portable Any Map – any three), Any Map – any three), PCDPCD(photo CD), (photo CD), PNGPNG (Portable Network Graphics), (Portable Network Graphics), etc.etc.

► Vector: PSVector: PS(postscript), (postscript), EPSEPS (embedded (embedded postscript), postscript), CDWCDW (CorelDraw), (CorelDraw), WMFWMF (windows metafile), (windows metafile), SVGSVG (Scalable vector (Scalable vector graphics), graphics), etc.etc.


CompuServ GIF – CompuServ GIF – Graphics Interchange FormatGraphics Interchange Format

► First standardized in 1987 by compuserv First standardized in 1987 by compuserv (called GIF87a)(called GIF87a)

► Updated in 1989 to include transparency, Updated in 1989 to include transparency, interlacing, and animation (called interlacing, and animation (called GIF89a)GIF89a)

► Use the LZW (Lempel-Ziv Welch) Use the LZW (Lempel-Ziv Welch) algorithm for compressionalgorithm for compression

► A maximum of 256 colors, so doesn’t A maximum of 256 colors, so doesn’t work well for photographswork well for photographs

► Suitable for small images such as iconsSuitable for small images such as icons► Simple animationsSimple animations


BitmapsBitmaps

►Can create great image with 24 or Can create great image with 24 or even 32 bits per pixeleven 32 bits per pixel

►File size is large, for example, a bitmap File size is large, for example, a bitmap image of size 1024*768*3= 2MBsimage of size 1024*768*3= 2MBs

►How to reduce size? Run Length How to reduce size? Run Length Encoding (RLE) – losslessEncoding (RLE) – lossless

►What about even smaller size? Lossy What about even smaller size? Lossy encoding such as JPEG.encoding such as JPEG.


JPEG JPEG ((Joint Photographic Experts Joint Photographic Experts GroupGroup))

►Lossy encodingLossy encoding


TIFF TIFF (Tag Image File Format)(Tag Image File Format)

► Tag-based image formatTag-based image format► Originated in 1986 at Aldus Corp. Originated in 1986 at Aldus Corp.

(PageMaker), the latest version 6.0(PageMaker), the latest version 6.0► Developed by Aldus and Microsoft Developed by Aldus and Microsoft ► Platform-independentPlatform-independent► Mostly used by scanners and desktop Mostly used by scanners and desktop

publishingpublishing► http://www.libtiff.org/ for a TIFF library for a TIFF library► Support compressions of CCITT Fax 3 & 4, Support compressions of CCITT Fax 3 & 4,

LZW, JPEG etc.LZW, JPEG etc.► Support multiple color spaces: Grayscale, Support multiple color spaces: Grayscale,

RGB, YCbCr, CMYK etc.RGB, YCbCr, CMYK etc.


Some detailsSome details

► File headerFile header- Byte order (2 bytes) : Byte order (2 bytes) :

MM or IIMM or II- Version ( 2 bytes) : 42 Version ( 2 bytes) : 42

(deep philosophical (deep philosophical reason!)reason!)

- Pointer to first IFD (4 Pointer to first IFD (4 bytes)bytes)

► IFD (image file IFD (image file directory)directory)

- Pointer count ( 2 Pointer count ( 2 bytes)bytes)

- Tagged pointer 0 (12 Tagged pointer 0 (12 bytes)bytes)

- Tagged pointer 1 (12 Tagged pointer 1 (12 bytes)bytes)

…………

-pointer to next IFD (if -pointer to next IFD (if none, 0000) (4 bytes)none, 0000) (4 bytes)


Some details - continuedSome details - continued

►Tagged pointer (12 bytes)Tagged pointer (12 bytes)- Tag code ( 2 bytes) : in the specsTag code ( 2 bytes) : in the specs- Type of data (2 bytes ) : 1 (BYTE), 2 Type of data (2 bytes ) : 1 (BYTE), 2

(ASCII), 3 (SHORT), 4 (LONG), 5 (ASCII), 3 (SHORT), 4 (LONG), 5 (rational)(rational)

- Length ( 4 bytes)Length ( 4 bytes)- Data pointer or data fieldData pointer or data field


Which One to UseWhich One to Use

►No unique answerNo unique answer►For small image e.g. icon …. GIFFor small image e.g. icon …. GIF►For large image e.g. photograph … For large image e.g. photograph …

JPEGJPEG► If scalability required … PS, EPSIf scalability required … PS, EPS

Digital Image Processing Lecture # 14 Color Image Processing

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