Color Image Processing

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

More Related Content

What's hot

Viewers also liked

Similar to Color Image Processing

Recently uploaded

Color Image Processing