There is a great variety of data formats in which to store
images. They can be divided into two types: one which describes
the contents of every pixel and another which describes how to
draw the objects in an image by means of graphical semantics.
In the absence of an image format converter, like the image
toolkit imtools of the San Diego Supercomputer Center,
it is important that the visualization software is capable of
understanding the formats most used. Below, we have summarized
some of these formats. The first two are based on a graphical
language but the others are pixel based.
CGM
CGM, the Computer Graphics Metafile, has been an ISO standard
since 1987. It has the capability to encompass both graphical and
image data.
PostScript
PostScript or more specifically Encapsulated PostScript Format
(EPSF), is a page description language with sophisticated text
facilities . For graphics, as compared to CGM, it tends to be
expensive in terms of storage.
TIFF
TIFF, the Tagged Image File Format, encompasses a range of
different formats, originally designed for interchange between
electronic publishing packages.
GIF
GIF, the Graphical Interchange Format , is quite widespread and
can encode a number of separate images of different sizes and
colors.
RGB
RGB, the Red Green Blue format of Silicon Graphics, is used by
most visualization software packages as the internal image
format. The format consist of a header containing the dimensions
of the image, followed by the actual image data. The image data
is stored as a 2D array of tupels. Each tupel is a vector with 3
components: R, G, and B. The RGB components determine the color
of every pixel (picture element) in the image.
PPM, PGM, PBM
PPM, the Portable Pixmap Format (24 bits per pixel), PGM, the
Portable Greyscale Format (8 bits per pixel), and PBM, the
Portable Bitmap Format (1 bit per pixel) formats are pixel based
and are distributed with the the X-Window system (version 11.4).
XBM
XBM is the X-Window one Bit image file format, which has been
standardized by the MIT X-consortium.A major constraint on the
use of images is the large data volume which has to be dealt
with. Large sets of image data can have severe implications for
storage, memory, and transmission costs. Therefore, compression
techniques are very important. There are two categories based on
whether or not it is possible to reconstruct the initial picture
after compression. They are:
Lossless methods
Lossless compression methods are methods for which the original,
uncompressed data can be recovered exactly. Examples of this
category are the Run Length Encoding, and the Lempel-Ziv Welch
algorithm.
Lossy methods
In contrast to lossless compression, the original data cannot be
recovered exactly after a lossy compression of the data. An
example of this category is the Color Cell Compression
method.Lossy compression techniques can reach reduction rates of
0.9, whereas lossless compression techniques normally have a
maximum reduction rate of 0.5.
