1. Chapter 6. Color Image Processing
2012 Spring
Image Processing
Hanyang University
ECE Division

2. Contents 6. Color Image Processing 6.1 Color Fundamentals
6.2 Color Models
6.3 Pseudocolor Image Processing
6.4 Basics of Full-Color Image Processing
6.5 Color Transformations
6.6 Smoothing and Sharpening
6.7 Color Segmentation
6.8 Noise in Color Images
6.9 Color Image Compression

3. 6. Color Image Processing
Preview
Two motivations in color image processing
In automated image analysis, color is a powerful descriptor (simplify object identification and extraction)
The human eye can discern thousands of color shades and intensities, compared to about two-dozen shades of gray
Two major areas
Full color (TV camera or color scanner)
Pseudo-color (particle monochrome intensity or range of intensities)

4. 6.1 Color Fundamentals Color spectrum In 1666, Sir Isaac Newton
When a beam of sunlight is passed through a glass prism
The emerging beam of light consists of a continuous spectrum of colors ranging from violet to red (not white)
Divided into six broad regions
Violet, blue, green, yellow, orange, and red
Each color blends smoothly into the next

5. 6.1 Color Fundamentals

6. 6.1 Color Fundamentals Visible light Color perception
Relatively, narrow band of frequencies in the electromagnetic energy spectrum
Color perception
The colors that human beings perceive in an object are determined by the nature of the light reflected from the object
Some shades of color
A body that favors reflectance in a limited range of the visible spectrum
White
A body that reflects light that is balanced in all visible wavelengths
사람이 인지하는 컬러는 사물로부터 반산 된 빛의 속성의 의해 결정됩니다.
shade : 어두운 부분

7. 6.1 Color Fundamentals
Color can only exist when three components are present: a viewer, an object, and light
When white light hits an object, it selectively blocks some colors and reflects others
Only the reflected colors contribute to the viewer's perception of color

9. 6.1 Color Fundamentals Achromatic light
Its only attribute is its intensity, or amount
Viewers see on a black and white television set
The term gray level refers to a scalar measure of intensity that rages from black, to grays, and finally to white

10. 6.1 Color Fundamentals Chromatic light
The electromagnetic energy spectrum from approximately 400 to 700 nm
Radiance [Watt (W)]
The total amount of energy that flows from the light source
Luminance [Lumens (lm)]
A measure of the amount of energy an observer perceives from a light source
Brightness
A subjective descriptor that is impossible to measure
The achromatic notion of intensity
One of the key factors in describing color sensation

11. 6.1 Color Fundamentals Primary colors of light : red, green, blue
The CIE specific wavelength values to the three primary colors
Blue= nm, green= nm, red= 700 nm
No mean that these three fixed RGB components acting alone can generate all spectrum colors
Secondary colors of light : cyan, magenta, yellow
Magenta: R+B, cyan: G+B, yellow: R+G
Mixing the three primaries, or a secondary with its opposite primary color, in the right intensities produces white light

12. 6.1 Color Fundamentals Primary colors of pigments or colorants
cyan, magenta, yellow
A primary color of pigments is defined as one that subtracts or absorbs a primary color of light and reflects or transmits the other two
Secondary colors of pigments or colorants
red, green, blue
Combination of the three pigment primaries, or a secondary with its opposite primary, produces black

13. 6.1 Color Fundamentals R B G f Magenta f(x)= i(x) r(x) Light Eyes R B
Yellow R B G f
Paper
Red

14. 6.1 Color Fundamentals Characteristics of colors Brightness Hue
The chromatic notion of intensity
Hue
An attribute associated with the dominant wavelength in a mixture of light waves
Representing dominant color as perceived by an observer
Saturation
Referring to relative purity or the amount of white mixed with a hue
Saturation is inversely proportional to the amount of white light

15. 6.1 Color Fundamentals
Hue and saturation taken together are called chromaticity
A color may be characterized by its brightness and chromaticity
The amounts of red, green, and blue needed to form any particular color are called the tristimulus values
Denoted X(red), Y(green), and Z(blue)

16. 6.1 Color Fundamentals

17. 6.1 Color Fundamentals Munsell color system(1898)
Specifies colors based on three color dimensions, hue, lightness and chroma. – perceptually uniform and independent dimensions.

18. 6.1 Color Fundamentals CIE 1931 RGB Color matching function
the entire range of human color perception could be covered
435.8nm(B), 546.1nm(G), 700nm(R)
2°observer : the color-sensitive cones resided within a 2° arc of the fovea
There is the minus sign : Need 6-ch

19. 6.1 Color Fundamentals CIE 1931 XYZ Color matching function
Overcomes minus sign problem.
2°observer
Perceptual non-uniformity

20. 6.1 Color Fundamentals Chromaticity diagram
Showing color composition as a function of x(R) and y(G)
For any value of x and y, z(B) = 1 – (x + y)
The positions of the various spectrum colors are indicated around the boundary of the tongue-shaped
The point of equal energy = equal fractions of the three primary colors = CIE standard for white light
Boundary : completely saturated
Useful for color mixing
A triangle(RGB) does not enclose the entire color region

21. 6.1 Color Fundamentals

22. 6.1 Color Fundamentals CIE standard illuminant
A : tungsten lamp, 2856K
C : daylight simulator, 6744K
D65, D50 : average daylight, 6504K
F : Fluorescent lamp
E : equal energy at all wavelength

23. 6.1 Color Fundamentals

24. 6.1 Color Fundamentals Perceptual uniformity CIE 1976 L*a*b*
CIE 1976 L*u*v*

25. 6.5.4 Tone and Color Corrections
CIE 1976 L*a*b*
Lab color is designed to approximate human vision
Uniform color space
Correspond to the three characteristics of color
It is an excellent decoupler of intensity and color.
It is useful for making in both image manipulation and image compression
Its gamut encompasses the entire visible spectrum
It can represent accurately the colors of any device
Display, print or scanner etc.
Device independent color model=>Color management systems

26. 6.5.4 Tone and Color Corrections
CIE 1976 L*a*b*
D65 standard illumination x
0.3127 y
0.329 Xw Yw
100 Zw

27. 6.1 Color Fundamentals
Color Gamut
The range of color representation of a display device

28. 6.2 Color Models
Color space conversion translating color from one device's space to another
Gamut mismatch

29. 6.2 Color Models The purpose of a color model
To facilitate the specification of colors in some standard
Color models are oriented either toward hardware or applications
Hardware-oriented
Color monitor or Video camera : RGB
Color printer : CMY
Color TV broadcast : YIQ ( I : inphase, q : quadrature)
Color image manipulation : HSI, HSV
Image processing : RGB, YIQ, HSI

30. 6.2 Color Models
Additive processes create color by adding light to a dark background (Monitors)
Subtractive processes use pigments or dyes to selectively block white light (Printers)

31. 6.2.1 The RGB Color Model
RGB model is based on a Cartesian coordinate system
The color subspace of interest is the cube
RGB values are at three corners
Colors are defined by vectors extending from the origin
For convenience, all color values have been normalized
All values of R, G, and B are in the range [0, 1]
직교 좌표계(直交 座標系, Rectangular coordinate system)는 임의의 차원의 에우클레이데스 공간 (혹은 좀더 일반적으로 내적공간)을 나타내는 좌표계의 하나이다. 이를 발명한 프랑스의 수학자 데카르트의 이름을 따 데카르트 좌표계(Cartesian coordinate system)라고도 부른다. 직교 좌표계는 극좌표계 등 다른 좌표계와 달리, 임의의 차원으로 쉽게 일반화할 수 있다. 직교 좌표계는 나타내는 대상이 평행이동(translation)에 대한 대칭을 가질 때 유용하나, 회전 대칭 등 다른 꼴의 대칭은 쉽게 나타내지 못한다. 일반적으로, 주어진 에우클레이데스 공간에 기저와 원점이 주어지면, 이를 이용하여 직교 좌표계를 정의할 수 있다.
가장 흔한 2차원 혹은 3차원의 경우, 직교 좌표를 통상적으로 라틴 문자 x, y, z로 적는다. 4차원인 경우, w나 (물리학에서 시공을 다루는 경우) t를 쓴다. 임의의 차원의 경우에는 첨자로 xn의 꼴로 쓴다.
3차원 좌표계 [편집]
오늘날 사용하는 3차원 좌표계는 xy, xz 그리고 yz 평면으로 이루어지는데 이 세 평면은 서로 직교하며, 평면을 이루는 x축(수평방향)과 y축(수직방향) 그리고 z축 또한 서로 직교한다. x, y, z축이 만나는 점을 원점이라고 부른다.
(points of equal RGB values)

32. 6.2.1 The RGB Color Model Images represented in the RGB color model
Images represented in the RGB color model consist of three independent image planes, one for each primary color
Pixel depth
The number of bits used to represent each pixel in RGB space is called the pixel depth
The term full-color image is used often to denote 24-bit RGB color image

33. 6.2.1 The RGB Color Model

34. 6.2.1 The RGB Color Model

35. 6.2.1 The RGB Color Model Example 6.1
Generating the hidden face planes and a cross section of the RGB color cube

36. 6.2.1 The RGB Color Model The set of safe RGB colors
A subset of colors that are likely to be reproduced faithfully, reasonably independently of viewer hardware capabilities
They are also called the set of all-system-safe colors, safe Web colors, safe browser colors
216 safe colors in RGB color model
On the assumption that 256 colors is the minimum number of colors that can be reproduced faithfully by any system, forty of these 256 colors are known to be processed differently by various operating systems, leaving only 216 colors that are common to most systems

37. 6.2.1 The RGB Color Model Safe RGB colors, or all-systems-safe colors
safe Web colors or
safe browser colors in internet applications
216 colors (= 63) are common to most systems
The de facto standard – the colors viewed by most people appear the same

38. 6.2.1 The RGB Color Model FFFFFF FFFFCC FFFF99 ?? FF0000 00FFFF
Decimal 51
102
204
HEX 33 66 CC
3366CC
FFFFCC
FFFF99 ??
FF0000
00FFFF
It is important to note that not all possible 8-bit gray colors are included in the 216 safe colors

39. 6.2.1 The RGB Color Model

40. 6.2.2 The CMY and CMYK Color Models
General purpose of CMY color model
To generate hardcopy output
The primary colors of pigments
Cyan, magenta, and yellow
C = W - R, M = W - G, and Y = W - B
Most devices that deposit colored pigments on paper require CMY data input
Converting RGB to CMY
The inverse operation from CMY to RGB is generally of no practical interest

41. 6.2.3 The HSI Color Model Usefulness
The intensity is decoupled from the color information
The hue and saturation are intimately related to the way in which human beings perceive color
An ideal tool for developing image procession algorithms based on some of the color sensing properties of the human visual system

42. 6.2.3 The HSI Color Model HSI color model H : hue S : saturation
I : intensity

43. 6.2.3 The HSI Color Model Hue and saturation in the HSI color model
Hue - Angle from the red axis
Saturation - Length of the vector

44. 6.2.3 The HSI Color Model
The HSI color model based on triangular and circular color planes

45. 6.2.3 The HSI Color Model Converting colors from RGB to HIS
Hue component
Saturation component
Intensity component
with
RGB values have been normalized to the range [0,1]
Angle is measured with respect to the red axis
Hue can be normalized to the range [0, 1] by dividing by 360c

46. 6.2.3 The HSI Color Model Converting colors from HSI to RGB
Three sectors of interest, corresponding to the 1200 intervals in the separation of primaries
RG sector

47. 6.2.3 The HSI Color Model Converting colors from HSI to RGB(continued)
GB sector
BR sector

48. Converting colors from HSI to RGB
Example 6.2
The HSI values corresponding to the image of the RGB color cube

49. Converting colors from HSI to RGB
Manipulating HSI component images

50. Converting colors from HSI to RGB

51. 6.3 Pseudocolor Image Processing
Pseudocolor image processing (or false color)
Pseudocolor image processing consists of assigning colors to gray values based on a specified criterion
The principal use of pseudocolor
Human visualization
Interpretation of gray-scale events
Pricipal motivation for using color
Humans can discern thousands of color shades and intensities, compared to only two dozen or so shades of gray

52. 6.3.1 Intensity Slicing Intensity Slicing
Intensity slicing can be expressed by the following equation

53. 6.3.1 Intensity Slicing

54. 6.3.1 Intensity Slicing
Example of intensity slicing

55. 6.3.1 Intensity Slicing Example 6.4
Use of color to highlight rainfall levels

56. 6.3.2 Gray Level to Color Transformations
Basic concept of the grey level to color transformation
Performing three independent transformations on the gray level of any input pixel

57. 6.3.2 Gray Level to Color Transformations

58. 6.3.2 Filtering Approach (not in textbook)
IFT
Additional
Processing
Color
display
monitor
Fourier
Transform
F(x,y)
Filter
IFT
Additional
Processing
Filter
IFT
Additional
Processing
A filtering model for pseudo-color image processing

59. 6.3.2 Filtering Approach (not in textbook)

60. 6.3.2 Gray Level to Color Transformations

61. 6.3.2 Gray Level to Color Transformations
Example 6.6
Color coding of multispectral images

62. 6.3.2 Gray Level to Color Transformations
Example 6.6 (continued)
Color coding of multispectral images

63. 6.4 Basics of Full-Color Image Processing
A pixel at (x,y) is a vector in the color space
RGB color space
c.f. gray-scale image
f(x,y) = I(x,y)

64. 6.4 Basics of Full-Color Image Processing

65. 6.5 Color Transformations
Similar to gray scale transformation
g(x,y)=T[f(x,y)]
Color transformation
g(x,y)
f(x,y) s1 s2 … sn f1 f2 fn T1 T2 Tn

66. 6.5.1 Formulation Formulation is a color input image
is the transformed or processed
is an operator
Transformation or color mapping functions
n is the number of color components
The color components of and at any point
is a set of transformation

67. 6.5.1 Formulation
Some operations are better suited to specific models.
In the HIS color space, this can be done with the simple transformation
In the RGB color space, three components must be transformed:
The CMY space requires a similar set of tranfomations:

68. 6.5.1 Formulation

69. 6.5.1 Formulation
It is important that to note that each transformation defined in Eqs.(6.5-4) through (6.5-6) depends only on one component within its color space

70. 6.5.2 Color Complements
Complements : the hues directly opposite one another on the color circle
Color complements are useful for enhancing detail that is embedded in dark regions of a color image.

71. 6.5.2 Color Complements

72. 6.5.3 Color Slicing
Highlighting a specific range of colors in an image is useful for separating objects from their surrondings.
The basic idea
Display the colors of interest so that they stand out from the background
Use the region defined by the colors as a mask for further processing
Gray-level slicing techniques

73. 6.5.3 Color Slicing The simplest ways to “slice”
To map the colors outside some range of interest to a nonprominent neutral color (see fig.6.43)
If the colors of interest are enclosed by a cube,
If a sphere is used to specify the colors of interest,

74. 6.5.3 Color Slicing How to take a region of colors of interest?
prototype color
prototype color
Sphere region
Cube region

75. 6.5.3 Color Slicing

76. 6.5.4 Tone and Color Corrections
Color transformation can be performed on most computers
Digital cameras, flatbed scanners and inkjet printers turn a personal computer into a digital darkroom allowing tonal adjustments and color corrections without the need for traditionally outfitted wet processing
The focus of current discussion is photo enhancement and color reproduction

77. 6.5.4 Tone and Color Corrections
Tonal transformation
Adjusts the image’s brightness and contrast to provide maximum detail over a suitable range of intensities.
The tonal range of an image, also called key type, refers to its general distribution of color intensities.
High-key: images are concentrated at high (or light) intensities.
Low-key: images are located predominantly at low intensities
Middle-key: images lie in between.

78. 6.5.4 Tone and Color Corrections

79. 6.5.4 Tone and Color Corrections
Note that the transformations depicted are the functions required for correcting the images(color balancing).

80. 6.5.5 Histogram Processing Histogram equalization Color images
A transformation that seeks to produce an image with a uniform histogram of intensity values
Color images
Composed of multiple components
The gray-scale technique to more than one component and/or histogram
A more logical approach
To spread the color intensities uniformly, leaving the colors themselves(e.g., hues) unchanged.
HSI color space is ideally suited to this type approach.

81. 6.5.5 Histogram Processing

82. 6.6 Smoothing and Sharpening
The next step beyond transforming each pixel of a color image without regard to its neighbors (as in the previous section) is to modify its value based on the characteristics of the surrounding pixels.
The basics of neighborhood processing are illustrated within the context of color image smoothing and sharpening.

83. 6.6.1 Color Image Smoothing The basics of neighborhood processing
As a spatial filtering operation in which the coefficients of the filtering mask are all 1’s
The mask is slid across the image to be smoothed
Each pixel is replaced by the average of the pixels in the neighborhood defined by the mask

84. 6.6.1 Color Image Smoothing

85. 6.6.1 Color Image Smoothing

86. 6.6.1 Color Image Smoothing

87. 6.6.2 Color Image Sharpening
Use the Laplacian (same as smoothing)

88. 6.7 Color Segmentation
Segmentation is a process that partitions an image into regions. Although segmentation is the topic of Chapter 10, we consider color segmentation briefly here for the sake of continuity.

89. 6.7.1 Segmentation in HSI Color Space
Color is conveniently represented in the hue image
Saturation is used as a masking image in order to isolate further regions of interest in the hue image
The intensity image
Not used segmentation
No color information

90. 6.7.1 Segmentation in HSI Color Space
Hue
Binary Saturation mask
Product of hue and mask
Saturation
Intensity
Thresholding

91. 6.7.2 Segmentation in RGB Vector Space
Segmentation is one area in which better results generally are obtained by using RGB color vectors than HSI space.
A measure similarity used the Euclidean distance
“average” color : the RGB vector a
z : an arbitrary point in RGB space

92. 6.7.2 Segmentation in RGB Vector Space
Generalized form
Implementing both equations is computationally expensive for images of practical size.
A compromise is to use a bounding box.

93. 6.7.2 Segmentation in RGB Vector Space

94. 6.7.3 Color Edge Detection By gradient operators
The gradient discussed in section is not defined for vector for quantities
Computing the gradient on individual images and then using the results to form a color image will lead to erroneous results

95. 6.7.3 Color Edge Detection
Definition of the gradient applicable to vector quantities : method by Di Zenzo
The gradient(magnitude and direction)of the vector c

96. 6.7.3 Color Edge Detection The direction of maximum rate of change of
The value of the rate of change at (x, y)

97. 6.7.3 Color Edge Detection

98. 6.7.3 Color Edge Detection

99. 6.8 Noise in Color Image The noise content of a color image
The same characteristics in each color channel
Possible for color channels to be affected differently by noise
Different noise level
Caused by differences in the relative strength of illumination available to each of the color channels
For example(use of a red filter : CCD camera)
CCD sensors are noisier at lower levels of illumination
Red component tend to be noisier than two components
In following slides, we take a look at noise in color image and how noise carries over when converting color model.

100. 6.8 Noise in Color Image

101. 6.8 Noise in Color Image
The hue and saturation components are degraded due to the nonlinearity of the cos and min operation.
The intensity image is the average of the RGB images.

102. 6.8 Noise in Color Image

103. 6.9 Color Image Compression
Data compression
A central role in the storage and transmission of color images
Data : the red, green, and blue components of the
pixels in an RGB image
The means by the color information is conveyed
Compression is the process of reducing or elimination redundant and/or irrelevant data

104. 6.9 Color Image Compression