CS3162 Introduction to Computer Graphics
Helena Wong, 2001
6. Coloring, Images, and File Formats
6.1 Color Models
A color model is a method for explaining the properties or behaviour of color
within some particular context.
Properties of Light and Colors
Light or colors are from a narrow frequency band within the electromagnetic
spectrum:
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
Hue (Color): The dominant frequency reflected by the object
Brightness: The perceived intensity of the light.
luminance of the source.
This is related to the
Purity (Saturation): How pure the color of the light appears.
Chromaticity: collectively refer to purity and hue.
Complementary colors: eg. Red and Cyan, Green and Magenta, Blue and
Yellow, which combine and form white light.
Primary colors: eg. r,g,b - starting from these colors, a wide range of other
colors can be formed.
Shades of a color: created by adding black pigment to the color.
Tints of a color: created by adding white pigment to the color.
Tones of a color: created by adding black or white pigment to the color.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
RGB Model
Used by RGB monitors which
separate signals for the red, green,
blue components of an image.
Based on the vision system of our
eyes, that we have 3 visual pigments
in the cones of the retina, for red,
green, and blue.
YIQ Model
Used by the NTSC television monitors:
Y - Contain the luminance (brightness) information
I - Contain the orange-cyan hue information
Q - Contain the green-magenta hue information
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
CMY Model
Useful for describing color output to hard-copy devices. These devices
produce a color picture by coating a paper with color pigments. We see the
colors by reflected light, which is a subtractive process.
CMYK means using the ink of Cyan, Magenta, Yellow, and Black.
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CS3162 Introduction to Computer Graphics
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Consider that,
- Magenta ink indeed subtracts the green component from incident light, so
the remaining red and blue components are seen by us, as a resultant color
of magenta.
- Cyan ink indeed subtracts the red component from incident light, so the
remaining green and blue components are seen by us, as a resultant color of
cyan.
- If we mix the ink of magenta and cyan, then, this ink subtracts the green and
red component from the incident light, and the remaining blue component is
seen by us, as a resultant color of blue.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
HSV Model
In this model, users select a spectral color
and apply the amounts of white or black that
are to be added to obtain different shades,
tints, and tones.
HSV model is derived from the RGB cube.
H: Hue, S: Saturation, V: Value
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
Saturation and value are measured as the
horizontal and vertical axes. And a degree (0 360) describes the hue. Complementary colors
are 180 degrees apart.
Interesting facts about human eyes:
- can distinguish about 128 different hues
- can distinguish about 130 different tints (saturation levels)
- can distinguish about 23 shades for yellow and about 16 different shades for
blue colors
- hence, can distinguish about 128 x 130 x 23 = 82720 different colors.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
HLS Model
Used by Tektronix.
H: Hue
L: Lightness
S: Saturation
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
6.2 Light Intensities
Values of intensity calculated by an illumination model must be converted to
one of the allowable intensity levels for the particular graphics system in use.
We have 2 issues to consider:
1. Human perceive relative light intensities on a logarithmic scale.
Eg. We perceive the difference between intensities 0.20 and 0.22 to be the
same as the difference between 0.80 and 0.88. Therefore, to display
successive intensity levels with equal perceived differences of brightness, the
intensity levels on the monitor should be spaced so that the ratio of successive
intensities is constant:
I1/I0 = I2/I1 = I3/I2 = … = a constant.
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CS3162 Introduction to Computer Graphics
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2. The intensities produced by display devices are not linear with the electrongun voltage.
This is solved by applying a gamma correction for video lookup correction:
Voltage for intensity Ik is computed as:
Vk = (Ik / a )1 / ?
Where a is a constant and ? is an adjustment factor controlled by the user.
For example, the NTSC signal standard is ?=2.2.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
6.3 Halftone Patterns and Dithering
Halftoning is used when an output device has a limited intensity range, but we
want to create an apparent increase in the number of available intensities.
Example: The following shows an original picture and the display of it in
output devices of limited intensity ranges (16 colors, 8 colors, 4 colors):
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If we view a very small area from a sufficiently
large viewing distance, our eyes average fine details
within the small area and record only the overall
intensity of the area.
By halftoning, each small resolution unit is
imprinted with a circle of black ink whose area is
proportional to the blackness of the area in the
original photograph.
Graphics output devices can approximate the
variable-area circles of halftone reproduction by
incorporating multiple pixel positions into the
display of each intensity value.
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A 2 x 2 pixel grid used to display 5 intensity levels (I) on a bilevel system:
A 3 x 3 pixel grid used to display 10 intensities on a bilevel system:
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CS3162 Introduction to Computer Graphics
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A 2x2 pixel grid used to display 13 intensities on a 4-level system:
An RGB 2x2 pixel-grid pattern:
Each r,g,b color has 4 phosphor dots
in the pattern, which allows 5
possible settings per color. This
gives a total of 125 different color
combinations.
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Dithering
The above approach, however, needs a higher resolution output device to
display a picture in the same physical dimensions.
So, in reality, we have to refine this approach so that it does not require for
higher resolution.
Dithering generally means to approximate halftone without this requirement.
Interested students may find further discussion in many text books.
Below are 2 examples of dithering results, using 4 and 2 colors respectively.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
6.4 Anti-Aliasing
On dealing with integer pixel positions, jagged or stairstep appearances
happen very usually. This distortion of information due to undersampling is
called aliasing. A number of antialiasing methods have been developed to
compensate this problem.
One way is to display objects at higher resolution. However there is a limit to
how big we can make the frame buffer and still maintaining acceptable refresh
rate.
Other methods modify pixel intensities by varying them along the boundaries
of primitives => smoothing the edges. These include supersampling, area
sampling, and pixel phasing.
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Supersampling
In supersampling, intensity information is obtained from multiple points that
contribute to the overall intensity of a pixel.
For straight lines with zero widths:
3 intensity values are used to paint the line
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For straight lines with finite widths:
9 intensity values are used to paint the line
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Pixel-Weighting Masks: Supersampling can be
implemented by giving more weight to sub-pixels
near the center of a pixel area.
1 2 1
2 4 2
1 2 1
Filtering Technique: Similar to pixel-weighting. Instead of using the grid of
weighting values, we imagine a continuous weighting surface covering the
pixel:
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Area Sampling
In area sampling, we set each pixel intensity proportional to the area of
overlap of the pixel.
Pixel Phasing
Move the electron beam to more nearly approximate positions
(Micropositioning).
Compensation for Line Intensity Differences
Diagonal lines normally appear less bright than
horizontal line. Some compensation should be done
to compensate for this effect by adjusting the
intensity of each line according to its slope.
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CS3162 Introduction to Computer Graphics
Helena Wong, 2001
Antialiasing Area Boundaries
To smooth area outlines, we may adjust each
pixel intensity at the boundary positions
according to the percent of pixel area that is
inside the boundary.
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CS3162 Introduction to Computer Graphics
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Temporal Aliasing
Aliasing problems also appear in temporal cases:
- The wheel is in fact rotating clockwisely. But it appears rotating counterclockwisely.
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CS3162 Introduction to Computer Graphics
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6.6 Graphic Files
Bitmap Images and Vector Images
Graphic file formats differ in the way they represent graphic information—
either as vector drawings or as bitmap images.
Some formats contain only vector drawings or bitmap images, but many
include both in the same file.
1. Bitmap images
Most image-editing applications generate bitmap images, also called raster
images.
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A bitmap image is resolution-dependent—that is, it contains a fixed number
of pixels to represent its image data. As a result, a bitmap image can lose
detail and appear jagged if viewed at a high magnification on-screen or
printed at too low a resolution.
Bitmap images are the best choice for representing gradations of shades and
color—for example, in photographs or painted images.
Common imaging applications provide a collection of tools to improve or
transform bitmap images, eg. Sharpening filters, Blurring filters (normal
blurring, Gaussian blur, Motion blur, Unsharp Mask)
Bitmap images are usually captured through input devices which support
certain image input standards, eg. TWAIN, ISIS (The Image and Scanner
Interface Specification).
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CS3162 Introduction to Computer Graphics
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2. Vector graphics
Some drawing applications create vector graphics, made of lines and curves
defined by mathematical objects called vectors. Vectors describe graphics
according to their geometric characteristics.
A vector graphic is resolution-independent—that is, it can be scaled to any
size and printed on any output device at any resolution without losing its
detail or clarity. As a result, vector graphics are the best choice for graphics
that must retain crisp lines when scaled to various sizes—for example, logos.
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CS3162 Introduction to Computer Graphics
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Graphic File Sizes
Generally graphic file size is proportional to the pixel dimensions of the
image. Images with more pixels may produce more detail at a given printed
size, but they require more disk space to store and may be slower to edit and
print.
Eg. a 1-inch-by-1-inch 200-dpi (dot per inch) image contains 200 x 200
pixels, which is four times as many pixels as a 1-inch-by-1-inch 100-dpi
image and so has four times the file size.
To determine the image resolution to use, one should consider the medium of
final distribution for the image. For on-screen display, the image resolution
only needs to match the typical monitor resolution (72 or 96 dpi). For printing
on laser printer, a resolution of at least 150 dpi is preferred.
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Note that, using too low a resolution for a printed image results in
pixelation— output with large, coarse-looking pixels. Using too high a
resolution (pixels smaller than what the output device can produce) increases
the file size and slows the printing of the image, and some printer may not
have enough internal memory to cope with the image.
Color Modes
To cope with different requirement and characteristics of image files, most
applications can handle multiple color modes.
1. Bitmap mode
Uses one of two color values (black or white) to represent the pixels in an
image. Images in Bitmap mode are called bitmapped, or 1-bit, images because
they have a bit depth of 1.
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2. Grayscale mode
Uses up to 256 shades of gray. Every pixel of a grayscale image has a
brightness value ranging from 0 (black) to 255 (white).
3. Indexed color mode
Uses at most 256 colors. When converting a true-color image to indexed
color, a color lookup table is built to store and index the colors in the image. If
a color in the original image does not appear in the table, the software chooses
the closest one or simulates the color using available colors.
By limiting the palette of colors, indexed color can reduce file size while
maintaining visual quality.
4. Multichannel mode
Uses 256 levels in each r,g,b channel.
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File Compression
Many image file formats use compression techniques to reduce the storage
space required by bitmap image data.
Compression techniques are distinguished by whether they remove detail and
color from the image.
Lossless techniques compress image data without removing detail; lossy
techniques compress images by removing detail.
- Run Length Encoding (RLE) is lossless. This method scans the bitmap row
by row. For each row, it divides the line of colors into run-lengths
according to the change of colors, then record for each color how many
pixels are to be painted.
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- Lemple-Zif-Welch (LZW) is lossless. Similarly it archives by identifying
repetitions of data. But this is a more complex approach which builds a
table of tokens as it encodes data of the whole image, so that if it identifies
one chunk of data and then encounters it again, it can store the same token
into the file. This technique is most useful in compressing images that
contain large areas of single color, such as screenshots or simple paint
images.
- Joint Photographic Experts Group (JPEG) is lossy. It achieves data
compression through sampling techniques in the context of digital signal
processing. It is best for continuous-tone images, such as photographs. You
can manipulate the compression parameters to choose between greater
compression or greater accuracy.
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Common File Formats
1. BMP
BMP is the standard Windows image format on DOS and Windowscompatible computers. The BMP format supports RGB, indexed-color,
grayscale, and Bitmap color modes.
2. GIF
The Graphics Interchange Format (GIF) is the file format commonly used to
display indexed-color graphics and images. GIF uses a LZW-compressed
format. Transparent color is supported.
3. JPEG format
The Joint Photographic Experts Group (JPEG) format is commonly used to
display photographs and other continuous-tone images. The JPEG format
supports CMYK, RGB, and grayscale color modes.
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4. PCX
The PCX format supports RGB, indexed-color, grayscale, and Bitmap color
modes. PCX supports the RLE compression method. Images can have a bit
depth of 1, 4, 8, or 24.
5. PDF
Portable Document Format (PDF) is used by Adobe Acrobat, Adobe’s
electronic publishing software. PDF files can represent both vector and bitmap
graphics, and can contain electronic document search and navigation features
such as electronic links.
6. Raw
The Raw format is a flexible file format for transferring files between
applications and computer platforms. Raw format consists of a stream of
bytes describing the color information in the file. Each pixel is described in
binary format, with 0 equaling black and 255 equaling white (for images with
16-bit channels, the white value is 65535).
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7. TIFF
The Tagged-Image File Format (TIFF) is used to exchange files between
applications and computer platforms. TIFF is a flexible bitmap image format
supported by virtually all painting, image-editing, and page-layout
applications. Also, virtually all desktop scanners can produce TIFF images.
The TIFF format supports CMYK, RGB, and grayscale files with alpha
channels, and Lab, indexed-color, and Bitmap files without alpha channels.
TIFF also supports LZW compression.
8. DXF (Data Exchange File) format is used by PC-based CAD Applications.
It is originally created for the AutoCAD system, and is a kind of script
language (text file).
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9. MPEG (Moving Picture Experts Group)
MPEG is for digital video (and sound) compression. It achieves high
compression rate by storing only the changes from one frame to another.
(lossy)
MPEG-1, the standard for storage and retrieval of moving pictures and audio
on storage media
MPEG-2, the standard for digital television
MPEG-4, the standard for multimedia applications
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