General image file formats

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, the Computer Graphics Metafile, has been an ISO standard since 1987. It has the capability to encompass both graphical and image data.

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, the Tagged Image File Format, encompasses a range of different formats, originally designed for interchange between electronic publishing packages.

GIF, the Graphical Interchange Format , is quite widespread and can encode a number of separate images of different sizes and colors.

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, 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 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.