# Hue Value Saturation Project by vwi12657

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```									CS 445 / 645
Introduction to Computer Graphics
Lecture 13
Color
Assignment 3
Due March 23
• Fourth years plan for thesis collision
• We’ll provide lots of details to keep the assignment doable

Morphing algorithm
Last Class
We discussed vision physiology and

Today:
Color perception
Color representations
Specifying Color
Color perception usually involves three quantities:
• Hue: Distinguishes between colors like red, green, blue, etc
• Saturation: How far the color is from a gray of equal intensity
• Lightness: The perceived intensity of a reflecting object

Sometimes lightness is called brightness if the object is
emitting light instead of reflecting it.
In order to use color precisely in computer graphics, we
need to be able to specify and measure colors.
Combining Colors

Shining colored lights      Mixing paint colors and
on a white ball       illuminating with white light
How Do Artists Do It?
Artists often specify color as tints, shades, and tones
of saturated (pure) pigments
• Tint: Gotten by adding white to a pure pigment,
decreasing saturation
black to a pure pigment,      White
decreasing lightness
Pure Color
• Tone: Gotten by adding       Grays    Tones
white and black to a pure
pigment                       Black
HSV Color Space
Computer scientists frequently use an intuitive
color space that corresponds to tint, shade, and
tone:
• Hue - The color we see (red, green, purple)
• Saturation - How far is the color from gray (pink is less
saturated than red, sky blue is less saturated than royal blue)
• Brightness (Luminance) - How bright is the color (how bright
are the lights illuminating the object?)
HSV Color Model
Hue (H) is the angle
around the vertical axis
Saturation (S) is a value
from 0 to 1 indicating
how far from the vertical
axis the color lies
Value (V) is the height of
the hexcone”
HSV Color Model
H       S       V       Color
0    1.0      1.0     Red
120    1.0      1.0     Green
240    1.0      1.0     Blue
*      0.0      1.0     White
*      0.0      0.5     Gray
*      *        0.0     Black
60    1.0      1.0     ?
270    0.5      1.0     ?
270    0.0      0.7     ?

Figure 15.16&15.17 from H&B
Intuitive Color Spaces
A top-down view of hexcone
HSV Color Space
A more intuitive color space          Saturation
Value
• H = Hue
• S = Saturation
• V = Value (or brightness)
Hue
Precise Color Specifications
• Pigment-mixing is subjective --- depends on human observer,
surrounding colors, lighting of the environment, etc
• We need an objective color specification
• Light is electromagnetic energy in the 400 to 700 nm wavelength
range
• Dominant wavelength is the wavelength of the color we “see”
• Excitation purity is the proportion of pure colored light to white light
• Luminance is the amount (or intensity) of the light
Electromagnetic Spectrum
Visible light frequencies range between ...
• Red = 4.3 x 1014 hertz (700nm)
• Violet = 7.5 x 1014 hertz (400nm)

Figures 15.1 from H&B
Visible Light
Hue = dominant frequency (highest peak)
Saturation = excitation purity (ratio of highest to rest)
Lightness = luminance (area under curve)

White Light
Figures 15.3-4 from H&B                       Orange Light
How well do we see color?
What color do we see the best?
• Yellow-green at 550 nm
What color do we see the worst?
• Blue at 440 nm
Flashback: Colortables (colormaps) for color storage
• Which RGB value gets the most bits?
Can perceive color differences of 10 nm at extremes (violet and red)
and 2 nm between blue and yellow
Metamers – different energy radiations look like the same color
Color perception also affected by surrounding light and adaptation
Just noticeable difference (JND)
128 fully saturated hues can be distinguished
Cannot perceive hue differences with less
saturated light.
Sensitivity to changes in saturation for a fixed hue
and brightness ranges from 16 to 23 depending
on hue.
Talked about representing intensities last lecture
Human Color Vision
Humans have 3 light sensitive pigments in their cones,
called L, M, and S
Each has a different
spectral response curve:

“Tristimulus” color theory
Color Spaces
Three types of cones suggests color is a 3D quantity. How
to define 3D color space?
Idea:
• Shine given wavelength () on a screen
• User must control three lasers producing three wavelengths (say
R=700nm, G=546nm, and B=436nm)
• Adjust intensity of RGB until colors are identical
• Note phosphors of TV are not perfect RGB
emitters as the results to right demonstrate
A Problem Exists
Exact target match ( ) with phosphors not possible
• Some red had to be added to target color to permit exact match using
“knobs” on RGB intensity output of CRT
• Equivalently (theoretically),
some red could have been
removed from CRT output
• Figure shows that red
phosphor must remove some
cyan for perfect match
• CRT phosphors cannot
remove cyan, so 500 nm
cannot be generated
CIE Color Space
No standard set of three wavelengths can be
combined to generate all other wavelengths.
The CIE (Commission Internationale d’Eclairage)
defined three hypothetical lights X, Y, and Z with
these spectra:
Idea: any wavelength  can
be matched perceptually                              x~R
y~G
by positive combinations                             z~B
of X, Y, and Z
CIE Color Space
The gamut of all colors perceivable is thus a three-
dimensional shape in X, Y, Z
Color = xX + yY + zZ
CIE Chromaticity Diagram (1931)

For simplicity, we often
project to the 2D plane
x+y+z=1
x = x / (x+y+z)
y = y / (x+y+z)
z=1–x-y
Device Color Gamuts
Since X, Y, and Z are hypothetical light sources,
no real device can produce the entire gamut of
perceivable color
Example: CRT monitor
Device Color Gamuts

We can use the CIE chromaticity diagram to
compare the gamuts of various devices:
Note, for example,
that a color printer
cannot reproduce
on a color monitor
A Problem With XYZ Colors
If we have two colors C1 and C2, and we add DC
to both of them, the differences between the
original and new colors will not be perceived to
be equal
This is due to the variation of the just noticeable
differences in saturated hues
XYZ space is not perceptually uniform
LUV space was created to address this problem
RGB Color Space (Color Cube)
Define colors with (r, g, b) amounts of red, green,
and blue
RGB Color Gamuts
The RGB color cube sits within CIE color space
something like this:
Converting Color Spaces
Simple matrix operation:

The transformation C2 = M-12 M1 C1 yields RGB on
monitor 2 that is equivalent to a given RGB on
monitor 1
YIQ Color Space
YIQ is the color model used for color TV in
America. Y is brightness, I (orange-cyan) &
Q (green-magenta) are color
• Note: Y is the same as CIE’s Y
• Result: Use the Y alone and backwards compatibility with
B/W TV!
• These days when you convert RGB image to B/W image, the
green and blue components are thrown away and red is used
to control shades of grey (usually)
Converting Color Spaces
Converting between color models can also be
expressed as such a matrix transform:

Note the relative unimportance of blue in
computing the Y
Perceptually Uniform Color Space
Color space in which Euclidean distance between
two colors in space is proportional to the
perceived distance
• CIE, RGB, not perceptually uniform
– Example with RGB
– LUV was created to be
perceptually uniform
The CMY Color Model
Cyan, magenta, and yellow are the complements
of red, green, and blue
• We can use them as filters to subtract from white
• The space is the same as RGB except the origin is white
This is useful for hardcopy
devices like laser printers
• If you put cyan ink on the page, no red light is reflected
• Add black as option (CMYK) to match equal parts CMY
Halftoning

A technique used in newspaper printing
Only two intensities are possible, blob of ink and no
blob of ink

But, the size of the blob can be varied
Also, the dither patterns of small dots can be used
Halftoning
Halftoning – dot size
Halftoning – Moire Patterns
Repeated use of same dot
pattern for particular