color theory dr. ayman eldeib · gives cooler colors. cool colors make objects tend to recede in a...
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SBE 306B: Computer Systems III
(Computer Graphics)
Color Theory
Dr. Ayman Eldeib
Systems & Biomedical Engineering Department
Spring 2020
Color Theory
Introduction to Color Theory
With colors you can set a mood, attract attention, or make
a statement.
Color can be your most powerful design element if you
learn to use it effectively.
Being able to use colors consciously and harmoniously can
help you create spectacular visualization and animation.
How to calculate the color of a pixel?
Color Theory is a system of rules and guidance for mixing various colors.
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Color Theory
The Foundation of Color
Light: electromagnetic
phenomenon (different
wavelength = different
color).
Light is composed of many
different colors and the
various mixtures of light
compose the colors that we
can see.
Color: Interaction of light
and eye-brain system.
At its core, color is light.
Color Wheel
Color Theory
The Foundation of Color
Primary Colors: Colors that can
not be created by mixing other
colors.
Secondary Colors: Colors
made by mixing primaries colors.
Intermediate (Tertiary) Colors:
Colors made by mixing primaries
and secondary colors.
Color Wheel
Cont’d
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Color Theory
The Foundation of Color glBegin (GL_TRIANGLES);
glVertex2iv (p1);
glVertex2iv (p2);
glVertex2iv (p3);
glEnd ( );
glColor3f (1.0, 0.0, 0.0); // red
glColor3f (0.0, 1.0, 0.0); // green
glColor3f (0.0, 0.0, 1.0); // blue
An OpenGL Example
Cont’d
Color Theory
Types of Color Theories
Subtractive Theory
Additive Theory
The subtractive, or pigment theory
deals with how white light is absorbed
and reflected off of colored surfaces.
The Additive, or light theory deals
with radiated and filtered light.
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Color Theory
Subtractive Color Theory
Black absorbs most light.
White reflects most light.
Colored Pigments absorb light and reflect only
the frequency of the pigment color.
All colors other than the pigment colors are
absorbed so this is called subtractive color
theory.
The primary colors in Subtractive Theory are:
Cyan ( C )Magenta ( M )Yellow ( Y )Black ( K )
Used in Printing
and Painting
Color Theory
Additive Color Theory
Black radiates no light.
White (sun) radiates all light.
Video is the process of capturing and radiating
light, therefore it uses Additive (Light) Theory
not Subtractive (Pigment) Theory.
The primary colors in Additive Theory are:
Red ( R )
Green ( G )
Blue ( B )
The primary colors add together to make white.
Light Theory is also called Additive Theory.
Used in Television,
theater lighting,
computer monitors,
and video production
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Color Theory
The Color Wheel
Colors on the wheel can be
described using three parameters:
Hue: degrees from 0˚ to 360˚
Saturation: brightness or
dullness
Value: lightness or darkness
Color Theory
The Color Wheel
Hue or Spectral Color is represented
as an angle to distinguish the
dominant color (frequency).
Primary Colors:
0˚ = Red
120˚ = Green
240˚ = Blue
Secondary Colors:
60˚ = Yellow
180˚ = Cyan
300˚ = Magenta
Cont’d
Hue
Angle Color
0 – 60 Red
60 – 120 Yellow
120 – 180 Green
180 – 240 Cyan
240 – 300 Blue
300 – 360 Magenta
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Color Theory
The Color Wheel
Saturation or Chroma is the intensity
of a color; i.e. defines the purity
(vibrancy) of the color to indicate if it
is mixed with other colors.
A highly saturated color is bright and
appears closer to the edge of the
wheel.
A more unsaturated color is dull.
A color with no saturation is
achromatic or in the grey scale.
Cont’d
Saturation
Color Theory
The Color Wheel
Value represents the luminescent contrast
value between black and white.
Cont’d
Value
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Color Theory
The Color Wheel – 3D
Three parameters to describe a color: Hue Chroma Value
Cont’d
Color Theory
What is the Tone?
Tone = Shade + Tint
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Color Theory
Systematic ways of selecting colors
Monochromatic
Complementary
Analogous
Warm
Cool
Color Schemes
Color Theory
Monochromatic
One Hue (Color) and its values of Tint and Shade
Color Schemes Cont’d
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Color Theory
Complementary
Color Schemes Cont’d
(note spelling--NOT complimentary)
Colors that are opposite on the wheel.
Two colors when mixed produce white
(gray in general).
High Contrast
Color Theory
Analogous
Color Schemes Cont’d
A selection of colors that are adjacent.
Minimal Contrast
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Color Theory
Warm
Color Schemes Cont’d
Right half of the wheel gives warmer colors.
Warm colors make objects look closer in a
painting or drawing.
The warm colors found
in fire and the Sun.
Color Theory
Cool
Color Schemes Cont’d
Left half of the wheel gives cooler colors.
Cool colors make objects tend to recede
in a composition.
The cool colors found in snow and ice.
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Color Theory
Color Spaces (or Models) The RGB Color Space - Computer Displays
The HSV Color Space
The HLS Color Space
The HSB Color Space
The CIE Color Space – Color Standard
The CMY Color Space - Printing
The YIQ, YUV, YCrCb Color Space – Television
Opponent Colors – Biological
The color space is a 3D space; hence, any color can be defined as a 3D vector where each element in the vector
represents one color component.
A color space is a
method by which we
can specify, create and
visualize color.
Color Theory
Color Spaces (or Models)
Used in Video and computer graphics
It is represented as the cube shown
with Cartesian coordinates.
Each of the three axes represents a
primary color.
Cont’d
RGB
Each point on or inside the cube represents a color where the
coordinates of this point represent the components of the primary colors
that contribute to the current color.
Each component should have a value from 0 to 255 (or from 0.0 to 1.0).
The grayscale from 0,0,0 to 255,255,255 (0,0,0 to 1,1,1) represents the
shades of gray from black to white.
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Color Theory
Color Spaces (or Models)
HSV (Hue, Saturation, Value)
Saturation and Value are represented in % or
within a range.
Cont’d
HSV
Value represents the brightness of the color.
Value is represented as the centerline of the cone.
Value 0.0 (0) (black) is at the lower tip of the cone.
Value 1.0 (100) (white) is at the center point of the upper circle.
Shades of gray are represented along this line between black and white.
The HSV color space is quite similar to the way in which humans
perceive color, which is not always the case with RGB.
Color Theory
RGB to HSV Transformation
The conversion from a RGB to a HSV model is described by these formulas:
v = max = max (r,g,b) min = min(r,g,b)
s = c/v where: c = chroma = max (r,g,b) - min (r,g,b)
h = depends on which of r,g,b is the maximum
The meaning of the variables:
r,g,b – the red, green, blue components of the RGB model
h,s,v - the hue, saturation, value components of HSV model
The components of the RGB model (r,g,b), saturation (s) and value (v) should have
values in the range [0,1], while the hue (h) should have values in the range [0,360]
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Color Theory
HSV to RGB Transformation
The meaning of the variables:
h,s,v - the hue, saturation, value components
of HSV model
r,g,b – the red, green, blue components of the
RGB model
c - chroma
m - the RGB component with the smallest value
x - an intermediate value used for computing
the RGB model
The conversion from a HSV to RGB a model is described by these formulas:
The components of the RGB model (r,g,b), saturation (s) and value (v) should have
values in the range [0,1], while the hue (h) should have values in the range [0,360]
Color Theory
Gamma Correction A typical CRT has a non-linear voltage-to-light transfer function with a
power law usually denoted by gamma.
Power-Law Transformations
Gamma Correction I′ = c * I1/γ
γ < 1 Useful for enhancing details in
the darker regions of the image at the
expense of detail in the brighter regions.
γ > 1 Useful for enhancing details in
the brighter regions of the image at the
expense of detail in the darker regions.
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Color Theory
Gamma Correction
Original γ = 0.6 γ = 0.4 γ = 0.3
Cont’d
Color Theory
Gamma Correction Cont’d
Original γ = 3.0 γ = 4.0 γ = 5.0
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Color Theory
Digital Image
A digital image is composed of pixels which can be
thought of as small dots on the screen.
A digital image is an instruction of how to color each
pixel.
In the general case we say that an image is of size m-
by-n if it is composed of m pixels in the vertical direction
and n pixels in the horizontal direction.
Let us say that we have an image on the format 512-by-
1024 pixels. This means that the data for the image
must contain information about 524288 pixels.
Color Theory
Color images have 3 values per pixel; monochrome images have 1 value per pixel.
each square is called a pixel
(for picture
element)
grid of squares, each of which
contains a single color
Digital Image
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Color Theory
A digital image, I, is a mapping from a 2D grid of
uniformly spaced discrete points, {p = (r,c)}, into a
set of positive integer values, {I( p)}, or a set of
vector values}.
At each column location in each row of I there is a
value.
The pair ( p, I( p) ) is called a “pixel” (for picture
element).
Pixel
Cont’dDigital Image
Color Theory
p = (r,c) is the pixel location indexed by row, r, and
column, c.
I( p) = I(r,c) is the value of the pixel at location p.
If I( p) is a single number then I is monochrome.
If I( p) is a vector (ordered list of numbers) then I has
multiple bands (e.g., a color image).
Pixel
Cont’dDigital Image
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Color Theory
01 00 05 00 03 00 02 00 00 03 01 01 01 01 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 02 00 01
03 30 3A 38 39 2D 1D 15 10 0E 0C 0A 0A 0A 09 06 08 07 06 06 05 05 07 07 04 05 04 04 06 02 01 02 02 02 02 07 01 02 02 03
03 22 1B 16 14 0A 08 0B 0A 0D 0B 0B 0C 06 07 05 05 06 06 06 03 07 04 06 05 09 05 04 05 01 04 04 02 03 03 04 02 04 03 02
00 0F 0B 04 10 07 09 07 08 09 09 08 05 08 08 05 09 03 08 05 02 08 08 06 06 04 02 05 03 02 05 05 00 02 02 04 04 00 00 03
00 07 09 0E 0C 07 08 0A 0A 0B 0F 0A 0C 07 06 0B 07 0B 05 0B 08 09 07 03 08 04 04 02 00 04 02 04 00 04 03 08 00 06 09 04
00 0E 0C 09 09 08 08 07 08 09 09 0A 05 08 07 07 07 09 08 0A 08 09 06 0A 03 09 07 06 06 03 05 03 01 06 02 03 07 01 04 04
02 0C 0B 0A 05 08 09 0A 0C 0A 0A 08 0A 0A 06 08 06 06 04 06 02 06 07 04 04 04 06 09 05 05 08 06 04 05 04 06 01 0A 03 02
02 0B 14 0F 0F 0D 0A 0E 0A 0C 0C 0E 0A 0C 0B 09 0A 09 0A 0A 09 0B 0B 05 0C 0C 0A 04 07 06 03 05 07 04 05 03 02 01 06 03
02 10 12 0B 10 0A 0D 0D 0B 0D 0C 0B 0B 0C 0D 0B 0B 0A 0A 0A 0B 0C 17 15 1C 15 0D 08 09 08 05 05 05 04 02 05 04 04 00 04
01 15 0E 10 12 0C 0D 0C 0C 0A 0B 0B 09 0C 0F 09 09 0D 07 0B 08 15 60 5D 61 59 33 0D 0A 07 08 08 05 03 06 07 01 03 05 02
02 12 10 0F 0E 10 10 0B 0C 0F 0F 0E 0C 10 0D 15 10 09 12 11 12 50 68 66 89 71 5E 3F 08 09 0A 09 0A 03 03 02 05 05 04 02
01 11 12 0C 11 13 10 10 0B 10 0F 0C 11 11 13 0D 0F 0D 0D 0B 25 7A 7F 79 6D 80 6E 54 0C 0D 09 0A 06 04 02 05 00 05 04 03
01 10 0F 0D 12 0E 10 0E 0F 13 13 11 13 17 11 0F 14 11 11 14 39 84 88 7E 8C 73 7A 5C 1E 05 0A 0F 0E 0C 05 02 04 03 06 05
02 0F 15 0D 18 11 0D 11 14 10 12 12 14 19 13 17 13 16 16 20 73 68 87 89 93 8B 83 69 43 07 0A 12 0A 0B 06 06 03 04 05 03
02 13 14 14 16 11 13 13 17 12 17 17 28 1E 1A 17 19 14 12 4F 7D 74 85 91 93 8C 7F 6F 5F 0B 09 12 0D 0C 02 04 07 04 05 04
02 21 18 15 16 1D 15 18 1E 36 5B 29 2C 19 29 4F AF BC AF AB 9E A1 97 82 70 9F AE AD A5 92 16 10 07 0E 0A 0C 08 05 0B 05
01 17 1B 1A 1A 2B 1B 2A 32 34 46 2C 1B 26 4C 40 BA BB B5 AE 95 94 84 7A 8A 9A B9 BB AD 9C 8A 15 09 09 05 0B 0D 0F 0B 07
00 1A 18 1C 1E 27 21 1D 3F 4E 32 25 1B 1B 93 46 AF AB B1 AC A4 93 89 91 86 90 AA 9F 91 97 AD 7F 0C 0B 0E 0B 0C 0C 09 05
01 38 2C 24 2E 51 59 4B 30 27 39 2B 2B 24 29 69 37 25 29 82 97 A1 AB AC B2 A6 A6 A0 89 69 0F 10 1C 18 14 10 10 0F 0C 0F
03 21 2A 27 22 5C 44 31 3F 33 1F 37 24 23 36 27 24 2B 4D 50 85 90 96 86 A3 A5 99 8D 7A 4E 0E 1B 15 20 0F 0F 16 12 13 0B
01 1D 1F 2B 20 21 48 2F 40 2F 2D 2A 25 2B 2C 20 25 25 26 3E 55 5E 62 6D 6D 6E 68 5E 43 0D 10 21 18 32 1A 13 10 13 15 10
04 27 2F 2A 28 21 3B 45 2E 3A 40 33 2D 2F 1F 1E 1B 20 37 3C 3F 3C 34 30 24 17 0D 0B 0E 11 1E 23 1B 25 14 0D 10 0F 12 0F
04 22 27 37 33 1A 1B 35 4A 1D 20 2C 2F 1F 1F 3B 34 1A 2A 38 44 1E 0C 0C 06 0C 10 12 1B 21 21 34 32 20 0B 0E 10 0D 0D 0F
02 32 22 33 29 20 22 19 30 35 1D 1E 16 19 18 1C 16 18 23 39 10 13 0E 0E 1A 15 15 13 1A 18 2C 2E 19 0F 0D 10 0E 0E 14 0D
01 33 36 23 31 29 20 19 1B 1E 17 1C 1F 1F 1F 1C 31 23 1C 2F 13 11 16 10 12 16 13 19 1B 17 19 1D 13 14 10 10 12 11 12 0D
01 28 31 34 24 30 23 19 18 28 2A 1D 1F 1D 1B 1E 1B 26 31 39 16 14 13 14 13 15 1B 22 1A 1E 1B 15 13 16 0C 0D 11 0E 12 0D
00 29 20 1C 2E 25 28 28 22 1E 20 1F 1F 1D 1B 1C 29 22 43 37 17 10 15 15 12 10 14 15 1B 1E 15 1A 11 10 14 13 14 17 12 11
01 25 28 2A 23 23 29 26 1E 1D 34 38 1B 1B 22 26 18 1A 4C 33 1C 11 14 14 14 10 10 18 17 1E 29 20 1A 15 12 17 0E 14 12 12
Cont’dDigital Image
Color Theory
Cont’dDigital Image
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Color Theory
Cont’dDigital Image
Color Theory
Representing Digital Images
The representation of an M×N numerical array as
0,0 0,1 0, 1
1,0 1,1 1, 1
1,0 1,1 1, 1
...
...
... ... ... ...
...
N
N
M M M N
a a a
a a aA
a a a
−
−
− − − −
=
Cont’dDigital Image
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Color Theory
Representing Digital Images
The representation of an M×N numerical array as
(0,0) (0,1) ... (0, 1)
(1,0) (1,1) ... (1, 1)( , )
... ... ... ...
( 1,0) ( 1,1) ... ( 1, 1)
f f f N
f f f Nf x y
f M f M f M N
−
− =
− − − −
Cont’dDigital Image
Color Theory
Image File Header Format
Fixed fields
sequence.
Predefined items
Fixed size
Fixed Format
Field 1 Field 2
Field n
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Color Theory
Variable sequence of
fields.
Variable number of
items
Unlimited field size
Variable size
Tags have fixed
size/format
Tagged Format
Tag 1 Field 1 Tag 2 Field 2
Tag i Field i …………...
Image File Header FormatCont’d
Color Theory
There are a number of file formats in which one may
store the images in files and retrieve them from files. These are known as image file format standards. Here we will present some of the most popularly used Image
file format standards. Tagged Image Format (.tif, .tiff): The .tif format is a very broad
format, which can handle anything from bitmaps to compressed
color palette images. The tiff format supports several compression
schemes, but is often used for uncompressed images as well. This
format is popular, relatively simple, and allows color.
Cont’dImage File Format
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Color Theory
JPEG (.jpg): It is the most widely used as a standard and includes a
variable lossy encoding as part of the standard. Most images you find on
the Internet are JPEG-images
MPEG (.mpg): This format is extensively used throughout the Web
and is used only for motion images. This uses compression, yielding
only lossy videos.
Graphics Interchange Format (.gif): This format supports 8-bit color
palette images and is not very popular among the image processing
researchers.
Postscript (.ps, .eps, .epsf): This image format is mainly used while
introducing images or figures in a book and for printing. In postscript
format, gray level images are represented by decimal/hex numerals
encoded in ASCII.
Cont’dImage File Format
Color Theory
Intensity image
This is the equivalent to a "grayscale image". It represents an image as a matrix where every element has a value corresponding to how bright/dark the pixel at the corresponding position should be colored. There are two ways to represent the number that represents the brightness of the pixel:
The double class (or data type). This assigns a floating number ("a
number with decimals") between 0 and 1 to each pixel. The value
0 corresponds to black and the value 1 corresponds to white.
The other class is called uint8 which assigns an integer between 0
and 255 to represent the brightness of a pixel. The value 0
corresponds to black and 255 to white.
Cont’dImage File Format
Binary image
This image format also stores an image as a matrix but can only color a pixel black or white (and nothing in between). It assigns a 0 for black and a 1 for white. In that sense, any pixel may be stored in 1 bit only.
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Color Theory
RGB image
This is another format for color images. It represents an image with three matrices of sizes matching the image format. Each matrix corresponds to one of the colors red, green or blue and gives an instruction of how much of each of these colors a certain pixel should use. Therefore, a pixel requires 24 bits to store its color. (Hence comes the term 24-bit image.) The number of different colors that can be represented by one pixel is 224 = 16777216 different colors.
Multiframe image
In some applications we want to study a sequence of images. This is very common in biological, medical imaging, and animation. For these cases, the multiframe format is a convenient way of working with a sequence of images.
Cont’dImage File Format
Color Theory
Indexed image
This is a practical way of representing color images. An indexed image stores an image as two matrices. The first matrix has the same size as the image and one number for each pixel. The second matrix is called the color map (Look-Up Tables - LUT) and its size may be different from the image. The numbers in the first matrix is an instruction of what number to use in the color map matrix.
For example, 8-bit Color Images in which the color at each pixel is represented using an 8-bit integer. This means that the maximum number of different colors that is associated with the pixel is 256. For each pixel, an index number is stored (in 8 bits). This introduces additional complexity into the image format as the LUT is usually attached to the image. When the image colors are altered, it will be required to generate a new LUT.
Cont’dImage File Format