1. Computer Graphics & Image Processing Chapter # 6 Color Image Processing

2. ALI JAVED
Lecturer
SOFTWARE ENGINEERING DEPARTMENT
U.E.T TAXILA :
Office Room #:: 7

3. Color Image Processing
The use of color Image Processing is motivated by two principal factors:
Color is a powerful descriptor
Humans can distinguish between thousands of color shades and intensities compared to about only two dozen shades of gray

4. Full Color Processing vs Pseudo-Color Processing
In Full Color Processing the image in question typically are acquired with a Full-Color sensor e.g. Color TV camera or Color Scanner
In Pseudo-color Processing the problem is of assigning a color to a particular monochrome intensity or a range of intensities

5. Color Spectrum

6. Electromagnetic Spectrum

7. Physical Background
Visible light: a narrow band of electromagnetic radiation → 380nm (blue) nm (red)
Wavelength: Each physically distinct colour corresponds to at least one wavelength in this band.

8. Color Fundamentals
The colors that humans and some animals perceive in an object are determined by the nature of light reflected from the object

9. Achromatic vs Chromatic Light
Achromatic (void of color) Light: Its only contribute is its ‘Intensity’ or amount
Chromatic Light: spans the electromagnetic spectrum from approximately 400 to 700nm

10. Quantities for description of quantity of Chromatic Source of Light
Three basic quantities are used to describe the quantity of a chromatic source of light:
Radiance
Luminance
Brightness

11. Radiance The total amount of Energy that flows from a Light Source
It is measured in Watts

12. Luminance
Luminance gives a measure of amount of energy an observer perceives from a light source (measured in Lumens(lm) )
For example light emitted from a source operating in Infrared region of Spectrum could have significant energy (Radiance) but a human observer will hardly perceive it so luminance is zero.

13. Brightness It is a subjective measure
It embodies the achromatic notion of intensity and is one of the key factors in describing color sensation

14. Human Perception
Detailed experimental evidences has established that the 6 to 7 million cones in the human eye can be divided into three principal sensing categories, corresponding roughly to red, green and blue
Approximately 65% of all cones are sensitive to Red Light, 33% are sensitive to Green Light and about 2% are sensitive to Blue Light (most sensitive)

15. Human Perception
Due to these absorption characteristic of Human Eye colors are seen as variable combinations of the so-called ‘Primary Colors’ Red, Green and Blue
The primary colors can be added to produce secondary colors of Light
Magenta (Red+Blue)
Cyan (Green+Blue)
Yellow (Red+Green)

16. Absorption of Light by red, green and blue cones in Human Eye
Mixing the three primaries or a secondary with its opposite primary colors in the right intensities produces white light

17. Primary Color of Light vs Primary Color of Pigments
Red, Green and Blue Colors are Primary Colors of Light
In Primary Color of Pigments a primary color is defined as the one that subtracts or absorbs a primary color of Light and reflects or transmits the other two
Therefore the Primary Colors of Pigments are Magenta, Cyan and Yellow and secondary colors are Red, Green and Blue
A proper combination of three pigment primaries or a secondary with its opposite primary produces Black
Color Television Reception is an example of the additive nature of Light Colors

19. Tri-Stimulus Values
The amount of Red, Green and Blue needed to form a particular color (denoted by X, Y and Z)
A color is then specified by its “Tri-chromatic Coefficients”
Thus x+y+z=1

20. Chromaticity Diagram z=1-x-y
Another approach for specifying colors is to use chromaticity diagram
Shows color compositions as a function of x(red) and y(green)
For any x and y the corresponding value of z(blue) can be obtained as
z=1-x-y

21. Chromaticity Diagram

22. Chromaticity Diagram
To determine the range of colors that can be obtained from the 3 given colors in the CD, we simply draw connecting lines to each of the three color points.
The result is a triangle and any color inside a triangle is produced by various combinations of the three initial colors.
The triangle shows a typical range of colors (called the color gamut) produced by RGB monitor
CD= Chromaticity Diagram

23. Color Models
The purpose of a color model (also called Color Space or Color System) is to facilitate the specification of colors in some standard way
A color model is a specification of a coordinate system and a subspace within that system where each color is represented by a single point
Color Models
RGB (Red, Green, Blue)
CMY (Cyan, Magenta, Yellow)
HSI (Hue, Saturation, Intensity)
YIQ (Luminance,In phase, Quadrature)
YUV (Y' stands for the luma component (the brightness) and U and V are the chrominance (color) components )
YIQ is the color space used by the NTSC color TV system, employed mainly in North and Central America, and Japan

24. RGB Model
Each color is represented in its primary color components Red, Green and Blue
This model is based on Cartesian Coordinate System

25. RGB Model
In this model, the primary colors are red, green, and blue. It is an additive model, in which colors are produced by adding components, with white having all colors present and black being the absence of any color.
This is the model used for active displays such as television and computer screens.
The RGB model is usually represented by a unit cube with one corner located at the origin of a three-dimensional color coordinate system, the axes being labeled R, G, B, and having a range of values [0, 1]. The origin (0, 0, 0) is considered black and the diagonally opposite corner (1, 1, 1) is called white. The line joining black to white represents a gray scale and has equal components of R, G, B.

26. RGB Color Cube The total number of colors in a 24 Bit image is
(28)3 =16,777,216 (> 16 million)

27. Generating RGB image

28. CMY and CMYK Color Model
Cyan, magenta, and yellow are the secondary colors with respect to the primary colors of red, green, and blue. However, in this subtractive model, they are the primary colors and red, green, and blue, are the secondaries. In this model, colors are formed by subtraction, where adding different pigments causes various colors not to be reflected and thus not to be seen. Here, white is the absence of colors, and black is the sum of all of them. This is generally the model used for printing.
Most devices that deposit color pigments on paper (such as Color Printers and Copiers) requires CMY data input or perform RGB to CMY conversion internally R G B C M Y
1.00 - =

29. CMY and CMYK Color Model
CMY is a Subtractive Color Model
Equal amounts of Pigment primaries (Cyan, Magenta and Yellow) should produce Black
In practice combining these colors for printing produces a “Muddy-Black” color
So in order to produce “True-Black” a fourth color “Black” is added giving rise to CMYK model

30. CMY Color Model

31. CMY Color Model

32. HSI Color Model Hue (dominant colour seen)
Wavelength of the pure colour observed in the signal.
Distinguishes red, yellow, green, etc.
More the 400 hues can be seen by the human eye.
Saturation (degree of dilution)
Inverse of the quantity of “white” present in the signal. A pure colour has 100% saturation, the white and grey have 0% saturation.
Distinguishes red from pink, marine blue from royal blue, etc.
About 20 saturation levels are visible per hue.
Intensity
Distinguishes the gray levels.

33. HSI Color Model Separates out intensity I from the coding
Two values (Hue & Saturation) encode chromaticity
Intensity encode monochrome part.
Hue and saturation of colors respond closely to the way humans perceive color, and thus this model is suited for interactive manipulation of color images .

35. Properties of HSI (HSV)
Hue H is defined by an angle
Saturation S models the purity of the color
I=(R+G+B)/3

36. Conversion from RGB to HSI
Given an image in RGB color format, the H component of each RGB pixel is obtained using the equation:

37. Conversion from HSI to RGB

38. Conversion from HSI to RGB

39. Pseudo-Color (False Color) Image Processing
Pseudo-color Image Processing consists of assigning colors to gray levels based on specific criterion
Generally, the eye cannot distinguish more than about 50 gray levels in an image. Thus subtle detail can easily be lost in looking at gray scale images. To enhance variations in gray level and make them more obvious, gray scale images are frequently pseudo-colored, where each gray scale (generally at least 256 levels for most displays) are mapped to a color level through a LUT. The eye is extremely sensitive to color and can distinguish thousands of color values in a picture.
LUT=lookup table

40. Pseudo-Coloring using LUT
CLUT(Color lookup table):: A mapping of a pixel value to a color value shown on a display device.
For example, in a grayscale image with levels 0, 1, 2, 3, and 4, pseudocoloring is a color lookup table that maps 0 to black, 1 to red, 2 to green, 3 to blue, and 4 to white.
LUT=lookup table

41. Intensity Slicing
The technique of intensity slicing or density slicing or color coding is one of the simplest example of Pseudo-color image processing

42. Intensity Slicing
The Gray Scale [0,L-1] is divided into L levels; where l0 represents Black (f(x,y)=0) and lL-1 represents white (f(x,y)=L-1)
Suppose that P planes perpendicular to the intensity axis are defined at levels l1,l2…..,lp
Then assuming that 0<P<L-1 the P planes partition the gray scale into P+1 intervals, V1,V2…….Vp+1

43. Intensity Slicing
Gray level to color assignments are made according to the relation:
f(x,y)= ck if f(x,y)€vk
Where ck is the color associated with the kth intensity interval vk defined by the partition planes at l=k-1 and l=k

44. An Alternative View of Intensity Slicing

47. Basics of Full Color Image Processing
Full color image processing fall into 2 categories.
In 1st category we process each component image individually and then form a composite processed color image from the individually processed component.
In 2nd category we work with color pixels directly. Because full color images have at least three components, color pixels are really vectors.
Let c represent an arbitrary vector in RGB color space:

48. Basics of Full Color Image Processing
Color components are the function of co-ordinates(x,y) so we can write it as:
For an image of size MxN there are MN such vectors, c(x,y), for x=0,1,2,…,M-1; y=0,1,2,…,N-1

49. Basics of Full Color Image Processing

50. Color Transformations
Color transformation can be represented by the expression ::
g(x,y)=T[f(x,y)]
f(x,y): input image
g(x,y): processed (output) image
T[*]: an operator on f defined over neighborhood of (x,y).
The pixel values here are triplets or quartets (i.e group of 3 or 4 values)

51. Color Transformations
Si=Ti(r1,r2,…,rn) i=1,2,3,….n
ri and Si are variables denoting the color components of f(x,y) and g(x,y) at any point (x,y).
n is the no of color components
{T1,T2,…..,Tn} is a set of transformation or color mapping functions.
Note that n transformations combine to produce a single transformation T

52. Color Transformations
The color space chosen determine the value of n.
If RGB color space is selected then n=3 & r1,r2,r3 denotes the red, blue and green components of the image.
If CMYK color space is selected then n=4 & r1,r2,r3,r4 denotes the cyan, hue, magenta and black components of the image.
Suppose we want to modify the intensity of the given image
using g(x,y)=k*f(x,y) where 0<k<1

53. Color Transformations
In HSI color space this can be done with the simple transformation s3=k*r3
where s1=r1 and s2=r2
Only intensity component r3 is modified.
In RGB color space 3 components must be transformed:
si=k*ri i=1,2,3.
Using k=0.7 the intensity of an image is decreased by 30%

54. Color Transformations

55. Color Complements
The hues opposite to one another on the Color Circle are called Complements.
Color Complement transformation is equivalent to image negative in Grayscale images

56. Color Complements

57. Color Slicing
Highlighting a specific range of colors in an image is useful for separating objects from their surroundings.
Display the colors of interest so that they are distinguished from background.
One way to slice a color image is to map the color outside some range of interest to a non prominent neutral color.

58. Histogram Processing
Color images are composed of multiple components, however it is not suitable to process each plane independently in case of histogram equalization. This results in erroneous color.
A more logical approach is to spread the color intensities uniformly, leaving the colors themselves( hue, saturation) unchanged.
HSI approach is ideally suited to this type of approach.

59. Color Image Smoothing
Color images can be smoothed in the same way as gray scale images, the difference is that instead of scalar gray level values we must deal with component vectors of the following form:
The average of the RGB component vector in this neighborhood is:

60. Color Image Smoothing
We recognize the components of this vector as the scalar images that would be obtained by independently smoothing each plane of the starting RGB image using conventional gray scale neighborhood processing.
Thus we conclude that smoothing by neighborhood averaging can be carried out on a per color plane basis.

61. Color Image Smoothing

62. Color Image Smoothing

63. Color Image Sharpening

64. Noise in Color Images
Noise in color images can be removed through various noise models which we use in Image Restoration in case the noise content of a color image has the same characteristics in each color channel.
But it is possible for color channels to be affected differently by noise so in this case noise are removed from the image by independently processing each plane
Remove noise by applying smoothing filters (e.g gaussian, average, median) to each plane individually and then combine the result.

65. Noise in Color Images

66. Color Image Compression
Compression is the process of reducing or eliminating redundant and/or irrelevant information
A compressed image is not directly displayable it must be decompressed before input to a color monitor.
In case if in a compressed image 1 bit of data represents 230 bits of data in the original image, then compressed image could be transmitted over internet in 1 minute as compared to original image which will take 4 hours to transmit.

67. Any question