SlideShare una empresa de Scribd logo
1 de 22
N.Gowthaman-P15274353
S.Kiruthika-P15274354
II M.Sc.,(Computer Science)
SUBMITTED TO
B.SOWBARNIKA M.Sc.,M.Phil.,
Assistant professor,
Department of computer science.
 Introduction
 Color Fundamental
 Color Models
 Pseudo color processing
 Basic of full color image processing
 Color Transformation
 Conclusion
 The characteristics of color image are distinguished by its properties
brightness, hue and saturation.
 simplifies object extraction and identification.
 Motivation to use color
 Brightness
Hue
Motivation to use color:
 Powerful descriptor that often simplifies object identification and extraction
from a scene
 Humans can discern thousands of colour shades and intensities, compared to
about only two dozen shades of gray
Hue:
Attribute associated with the dominant wavelength in a mixture of light
waves
Hue is somewhat synonymous to what we usually refer to as "colors". Red,
green, blue, yellow, and orange are a few examples of different hues.
Mean wavelength of the spectrum
Brightness:
Intensity
Perceived luminance
Depends on surrounding luminance
Color Fundamental:
In 1666 Sir Isaac Newton discovered that when a beam of sunlight passes
through a glass prism, the emerging beam is split into a spectrum of colors
 A chromatic light source, there are 3 attributes to describe the quality:
 Primary colors can be added to produce the secondary colors of light:
 Cyan (green plus blue)
 Yellow (red plus green)
 Magenta (red plus blue)
 The three basic quantitles useds to describe the quantity of a chromatic light
source are:
 Radiance
 Luminance
Brightness
Radiance:
 The total amount of energy that flows from the light source (measured in watts)
Luminance:
 The amount of energy an observer perceives from the light source (measured in
lumens)
 we can have high radiance, but low luminance
Brightness:
 A subjective (practically unmeasurable) notion that embodies the intensity of light
 Color, by defining a 3D coordinate system, and a subspace that
contains all constructible colors within a particular model.
A color model is an abstract mathematical model describing the
way colors can be represented as tuples of numbers, typically as three or
four values or color components.
 Each color model is oriented towards either specific hardware
(RGB,CMY,YIQ), or image processing applications (HSI).
Any color that can be specified using a model will correspond to a single
point within the subspace it defines
TYPES OF COLOR MODELS:
 RGB Model
 CMY Model
 HSI Model
 YIQ Model
RGB Model:
 Color monitor, color video cameras
 In the RGB model, an image consists of three independent image planes,
one in each of the primary colors: red, green and blue.
 Specifying a particular colour is by specifying the amount of each of the primary
components present.
 The geometry of the RGB colour model for specifying colors using a Cartesian
coordinate system. The greyscale spectrum,
.
 The RGB color cube. The grayscale spectrum lies on the line joining the
black and white vertices.
CMY Model:
 The CMY (cyan-magenta-yellow) model is a subtractive model appropriate
to absorption of colors, for example due to pigments in paints
 Whereas the RGB model asks what is added to black to get a particular color,
the CMY model asks what is subtracted from white.
 In this case, the primaries are cyan, magenta and yellow, with red, green and
blue as secondary colors
 The relationship between the RGB and CMY
HSI Model:
 As mentioned above, colour may be specified by the three quantities hue,
saturation and intensity.
 This is the HSI model, and the entire space of colors that may be specified in
this way is shown
 Conversion between the RGB model and the HSI model is quite
complicated. The intensity is given by
I =R+G+B
 where the quantities R, G and B are the amounts of the red, green and blue
components, normalised to the range [0,1]. The intensity is therefore just the
average of the red, green and blue components.
 The saturation is given by:S = 1 –min
YIQ Model:
 The YIQ (luminance-inphase-quadrature)model is a recoding of RGB
for colour television, and is a very important model for colour image
processing. The importance of luminance was discussed in
 The conversion from RGB to YIQ is given by:
 The luminance (Y) component contains all the information required
for black and white television, and captures our perception of the
relative brightness particular colors.
Pseudo color image processing consists of assigning colors to grey
values based on a specific criterion
The principle use of pseudo color image processing is for human
visualization
Intensity slicing and color coding is one of the simplest kinds of
pseudo color image processing
 Grey level color assignments can then be made according to the
relation
where ck is the color associated with the kth intensity level Vk
defined by the partitioning planes at l = k – 1 and l = k
 Used in the case where there are many monochrome images such as
multispectral satellite images
 Full‐color image processing approaches fall into two major categories
 In the first category, we process each component image individually and
then form a composite processed color image from the individually
processed components
 In the second category, we work with color pixels directly
Color transformations:
 Color transformations can be of the form
 where ri and si are the color components of the input and output images, n
is the dimension of the color space. Ti are referred to as full‐color
transformation or mapping functions
Implementation Tips :
 Linear interpolation by using control points is implemented in
“interp1q”
 Cubic spline interpolation by using control points is Color
implemented in “spline”
 Digital color processing includes processing of colored images and
different color spaces that are used. For example RGB color model,
YCbCr,
 HSV. It also involves studying transmission , storage , and encoding of
these color images.
 The RGB primary commonly used for color display mixes the luminance
and chrominance attributes of a light.
THANKS TO
P. MEERABAI M.C.A.,M.Phil.,
HEAD OF THE DEPARTMENT,
COMPUTER SCIENCE.
Color Image Processing

Más contenido relacionado

La actualidad más candente

Image enhancement
Image enhancementImage enhancement
Image enhancement
Ayaelshiwi
 

La actualidad más candente (20)

Chapter 6 color image processing
Chapter 6 color image processingChapter 6 color image processing
Chapter 6 color image processing
 
Spatial Filters (Digital Image Processing)
Spatial Filters (Digital Image Processing)Spatial Filters (Digital Image Processing)
Spatial Filters (Digital Image Processing)
 
SPATIAL FILTERING IN IMAGE PROCESSING
SPATIAL FILTERING IN IMAGE PROCESSINGSPATIAL FILTERING IN IMAGE PROCESSING
SPATIAL FILTERING IN IMAGE PROCESSING
 
Image enhancement
Image enhancementImage enhancement
Image enhancement
 
Chapter10 image segmentation
Chapter10 image segmentationChapter10 image segmentation
Chapter10 image segmentation
 
Image Restoration
Image RestorationImage Restoration
Image Restoration
 
Simultaneous Smoothing and Sharpening of Color Images
Simultaneous Smoothing and Sharpening of Color ImagesSimultaneous Smoothing and Sharpening of Color Images
Simultaneous Smoothing and Sharpening of Color Images
 
Chapter 3 image enhancement (spatial domain)
Chapter 3 image enhancement (spatial domain)Chapter 3 image enhancement (spatial domain)
Chapter 3 image enhancement (spatial domain)
 
Image compression models
Image compression modelsImage compression models
Image compression models
 
Image Enhancement in Spatial Domain
Image Enhancement in Spatial DomainImage Enhancement in Spatial Domain
Image Enhancement in Spatial Domain
 
Color fundamentals and color models - Digital Image Processing
Color fundamentals and color models - Digital Image ProcessingColor fundamentals and color models - Digital Image Processing
Color fundamentals and color models - Digital Image Processing
 
Image Restoration (Digital Image Processing)
Image Restoration (Digital Image Processing)Image Restoration (Digital Image Processing)
Image Restoration (Digital Image Processing)
 
Bit plane coding
Bit plane codingBit plane coding
Bit plane coding
 
Digital Image Processing - Image Compression
Digital Image Processing - Image CompressionDigital Image Processing - Image Compression
Digital Image Processing - Image Compression
 
Image Smoothing using Frequency Domain Filters
Image Smoothing using Frequency Domain FiltersImage Smoothing using Frequency Domain Filters
Image Smoothing using Frequency Domain Filters
 
Hough Transform By Md.Nazmul Islam
Hough Transform By Md.Nazmul IslamHough Transform By Md.Nazmul Islam
Hough Transform By Md.Nazmul Islam
 
Image segmentation
Image segmentationImage segmentation
Image segmentation
 
Digital Image Processing
Digital Image ProcessingDigital Image Processing
Digital Image Processing
 
image compression ppt
image compression pptimage compression ppt
image compression ppt
 
Image enhancement
Image enhancementImage enhancement
Image enhancement
 

Destacado (11)

10 color image processing
10 color image processing10 color image processing
10 color image processing
 
Color models
Color modelsColor models
Color models
 
Colour models
Colour modelsColour models
Colour models
 
Color models
Color modelsColor models
Color models
 
Color image processing Presentation
Color image processing PresentationColor image processing Presentation
Color image processing Presentation
 
Digital image processing question bank
Digital image processing question bankDigital image processing question bank
Digital image processing question bank
 
Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part)Lecture 3b: Decision Trees (1 part)
Lecture 3b: Decision Trees (1 part)
 
Color Models Computer Graphics
Color Models Computer GraphicsColor Models Computer Graphics
Color Models Computer Graphics
 
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain RatioLecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
Lecture 4 Decision Trees (2): Entropy, Information Gain, Gain Ratio
 
Color Models
Color ModelsColor Models
Color Models
 
Digital image processing img smoothning
Digital image processing img smoothningDigital image processing img smoothning
Digital image processing img smoothning
 

Similar a Color Image Processing

Project report_DTRL_subrat
Project report_DTRL_subratProject report_DTRL_subrat
Project report_DTRL_subrat
Subrat Prasad
 
Lecnoninecolorspacemodelindigitalimageprocess
LecnoninecolorspacemodelindigitalimageprocessLecnoninecolorspacemodelindigitalimageprocess
Lecnoninecolorspacemodelindigitalimageprocess
IrsaAamir
 
Color Image Processing.pptx
Color Image Processing.pptxColor Image Processing.pptx
Color Image Processing.pptx
Antu Chowdhury
 

Similar a Color Image Processing (20)

Color-in-Digital-Image-Processing.pptx
Color-in-Digital-Image-Processing.pptxColor-in-Digital-Image-Processing.pptx
Color-in-Digital-Image-Processing.pptx
 
Color image processing
Color image processingColor image processing
Color image processing
 
Project report_DTRL_subrat
Project report_DTRL_subratProject report_DTRL_subrat
Project report_DTRL_subrat
 
Id3115321536
Id3115321536Id3115321536
Id3115321536
 
Color image processing ppt
Color image processing pptColor image processing ppt
Color image processing ppt
 
ch1ip.ppt
ch1ip.pptch1ip.ppt
ch1ip.ppt
 
Color image processing.ppt
Color image processing.pptColor image processing.ppt
Color image processing.ppt
 
Lecnoninecolorspacemodelindigitalimageprocess
LecnoninecolorspacemodelindigitalimageprocessLecnoninecolorspacemodelindigitalimageprocess
Lecnoninecolorspacemodelindigitalimageprocess
 
Colormodels
ColormodelsColormodels
Colormodels
 
06 color image processing
06 color image processing06 color image processing
06 color image processing
 
An investigation for steganography using different color system
An investigation for steganography using different color systemAn investigation for steganography using different color system
An investigation for steganography using different color system
 
Color
ColorColor
Color
 
Colormodels
ColormodelsColormodels
Colormodels
 
Color Image Processing.pptx
Color Image Processing.pptxColor Image Processing.pptx
Color Image Processing.pptx
 
Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extract...
Colorization of Gray Scale Images in YCbCr Color Space Using  Texture Extract...Colorization of Gray Scale Images in YCbCr Color Space Using  Texture Extract...
Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extract...
 
Question bank for students.pdf
Question bank for students.pdfQuestion bank for students.pdf
Question bank for students.pdf
 
Color image processing
Color image processingColor image processing
Color image processing
 
Color_Spaces.pptx
Color_Spaces.pptxColor_Spaces.pptx
Color_Spaces.pptx
 
Compututer Graphics - Color Modeling And Rendering
Compututer Graphics - Color Modeling And RenderingCompututer Graphics - Color Modeling And Rendering
Compututer Graphics - Color Modeling And Rendering
 
Lect 06
Lect 06 Lect 06
Lect 06
 

Último

Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 

Último (20)

Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Micro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdfMicro-Scholarship, What it is, How can it help me.pdf
Micro-Scholarship, What it is, How can it help me.pdf
 
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-IIFood Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
Food Chain and Food Web (Ecosystem) EVS, B. Pharmacy 1st Year, Sem-II
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural ResourcesEnergy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
Energy Resources. ( B. Pharmacy, 1st Year, Sem-II) Natural Resources
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
psychiatric nursing HISTORY COLLECTION .docx
psychiatric  nursing HISTORY  COLLECTION  .docxpsychiatric  nursing HISTORY  COLLECTION  .docx
psychiatric nursing HISTORY COLLECTION .docx
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17  How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17  How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
General Principles of Intellectual Property: Concepts of Intellectual Proper...
General Principles of Intellectual Property: Concepts of Intellectual  Proper...General Principles of Intellectual Property: Concepts of Intellectual  Proper...
General Principles of Intellectual Property: Concepts of Intellectual Proper...
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 

Color Image Processing

  • 1.
  • 3. SUBMITTED TO B.SOWBARNIKA M.Sc.,M.Phil., Assistant professor, Department of computer science.
  • 4.  Introduction  Color Fundamental  Color Models  Pseudo color processing  Basic of full color image processing  Color Transformation  Conclusion
  • 5.  The characteristics of color image are distinguished by its properties brightness, hue and saturation.  simplifies object extraction and identification.  Motivation to use color  Brightness Hue Motivation to use color:  Powerful descriptor that often simplifies object identification and extraction from a scene  Humans can discern thousands of colour shades and intensities, compared to about only two dozen shades of gray
  • 6. Hue: Attribute associated with the dominant wavelength in a mixture of light waves Hue is somewhat synonymous to what we usually refer to as "colors". Red, green, blue, yellow, and orange are a few examples of different hues. Mean wavelength of the spectrum Brightness: Intensity Perceived luminance Depends on surrounding luminance Color Fundamental: In 1666 Sir Isaac Newton discovered that when a beam of sunlight passes through a glass prism, the emerging beam is split into a spectrum of colors
  • 7.  A chromatic light source, there are 3 attributes to describe the quality:  Primary colors can be added to produce the secondary colors of light:  Cyan (green plus blue)  Yellow (red plus green)  Magenta (red plus blue)
  • 8.  The three basic quantitles useds to describe the quantity of a chromatic light source are:  Radiance  Luminance Brightness Radiance:  The total amount of energy that flows from the light source (measured in watts)
  • 9. Luminance:  The amount of energy an observer perceives from the light source (measured in lumens)  we can have high radiance, but low luminance Brightness:  A subjective (practically unmeasurable) notion that embodies the intensity of light
  • 10.  Color, by defining a 3D coordinate system, and a subspace that contains all constructible colors within a particular model. A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components.  Each color model is oriented towards either specific hardware (RGB,CMY,YIQ), or image processing applications (HSI). Any color that can be specified using a model will correspond to a single point within the subspace it defines
  • 11. TYPES OF COLOR MODELS:  RGB Model  CMY Model  HSI Model  YIQ Model RGB Model:  Color monitor, color video cameras  In the RGB model, an image consists of three independent image planes, one in each of the primary colors: red, green and blue.  Specifying a particular colour is by specifying the amount of each of the primary components present.  The geometry of the RGB colour model for specifying colors using a Cartesian coordinate system. The greyscale spectrum, .
  • 12.  The RGB color cube. The grayscale spectrum lies on the line joining the black and white vertices. CMY Model:  The CMY (cyan-magenta-yellow) model is a subtractive model appropriate to absorption of colors, for example due to pigments in paints  Whereas the RGB model asks what is added to black to get a particular color, the CMY model asks what is subtracted from white.  In this case, the primaries are cyan, magenta and yellow, with red, green and blue as secondary colors
  • 13.  The relationship between the RGB and CMY HSI Model:  As mentioned above, colour may be specified by the three quantities hue, saturation and intensity.  This is the HSI model, and the entire space of colors that may be specified in this way is shown
  • 14.  Conversion between the RGB model and the HSI model is quite complicated. The intensity is given by I =R+G+B  where the quantities R, G and B are the amounts of the red, green and blue components, normalised to the range [0,1]. The intensity is therefore just the average of the red, green and blue components.  The saturation is given by:S = 1 –min
  • 15. YIQ Model:  The YIQ (luminance-inphase-quadrature)model is a recoding of RGB for colour television, and is a very important model for colour image processing. The importance of luminance was discussed in  The conversion from RGB to YIQ is given by:  The luminance (Y) component contains all the information required for black and white television, and captures our perception of the relative brightness particular colors.
  • 16. Pseudo color image processing consists of assigning colors to grey values based on a specific criterion The principle use of pseudo color image processing is for human visualization Intensity slicing and color coding is one of the simplest kinds of pseudo color image processing  Grey level color assignments can then be made according to the relation where ck is the color associated with the kth intensity level Vk defined by the partitioning planes at l = k – 1 and l = k
  • 17.  Used in the case where there are many monochrome images such as multispectral satellite images
  • 18.  Full‐color image processing approaches fall into two major categories  In the first category, we process each component image individually and then form a composite processed color image from the individually processed components  In the second category, we work with color pixels directly Color transformations:  Color transformations can be of the form  where ri and si are the color components of the input and output images, n is the dimension of the color space. Ti are referred to as full‐color transformation or mapping functions
  • 19. Implementation Tips :  Linear interpolation by using control points is implemented in “interp1q”  Cubic spline interpolation by using control points is Color implemented in “spline”
  • 20.  Digital color processing includes processing of colored images and different color spaces that are used. For example RGB color model, YCbCr,  HSV. It also involves studying transmission , storage , and encoding of these color images.  The RGB primary commonly used for color display mixes the luminance and chrominance attributes of a light.
  • 21. THANKS TO P. MEERABAI M.C.A.,M.Phil., HEAD OF THE DEPARTMENT, COMPUTER SCIENCE.