Page 217 - DCAP303_MULTIMEDIA_SYSTEMS

Unit 12: Image Format

Fractal compression differs from other lossy compression methods, such as JPEG, in a number of notes
ways. The JPEG achieves compression by discarding image data that is not required for the human
eye to perceive the image. The resulting data is then further compressed using a lossless method
of compression. To achieve greater compression ratios, more image data must be discarded,
resulting in a poorer quality image with a pixelized (blocky) appearance.
Fractal images are not based on a map of pixels, nor is the encoding weighted to the visual
characteristics of the human eye. Instead, bitmap data is discarded when it is required to create
a best-fit fractal pattern. Greater compression ratios are achieved using greater computationally
intensive transforms that may degrade the image, but the distortion appears much more natural
due to the fractal components.
Most other loss methods are also symmetrical in nature. That is, a particular sequence of steps is
used to compress an image, and the reverse of those steps is used to decompress it. Compression
and decompression will take about the same amount of time as well. Fractal compression is an
asymmetrical process, taking much longer to compress an image than to decompress it. This
characteristic limits the usefulness of fractally compressed data to applications where image data
is constantly decompressed but never recompressed. Fractal compression is therefore highly
suited for use in image databases and CD-ROM applications.

The content and resolution of the source bitmap can greatly affect fractal compression. Images
with a high fractal content (e.g., faces, landscapes, and intricate textures) result in much higher
compression ratios than images with a low fractal content (e.g., charts, diagrams, text, and flat
textures). High-resolution images may be compressed to achieve higher compression ratios and
will still retain a high image quality. To retain a high quality for lower resolution images, the
resulting compression ratio will be much lower. Images with a greater bit depth (such as 24-bit
true colour images) will also compress more efficiently than images with fewer bits per pixel
(such as 8-bit gray-scale images).
The process of fractal compression is by no means in the public domain. There are many patents
claiming a method of image data compression based on fractal transforms. Also, the exact
process used by some fractal packages—including Barnsley’s Fractal Transform—is considered
proprietary.

the Humble Gif

the Birth of the Gif
In 1987, CompuServe released the GIF format for graphics, as a free and open specification.
In other words, any Web developer or graphics creator was free to create, post, trade, fold,
spindle, and mutilate GIFs as they saw fit. The creators used the LZW (Lempel Ziv Welch)
method of data compression to reduce the size of the files, and herein lies the copyright issue.
Note that the LZW compression method is also used in TIFF graphics and several older file
compression utilities, but because relatively few people use either TIFF graphics or these older
file compression programs, I will focus on the usage of GIFs.
The LZW was described by Terry A. Welch in the June 1984 issue of IEEE’s Computer magazine.
Unisys held, and still holds a patent, but describing the algorithm made no mention of this.
Welch, a Sperry employee, extended the work of previous developers Lempel and Ziv. Sperry
Corporation was granted the U.S. patent in 1985. Sperry and Burroughs merged in 1986 to form
Unisys, thus Unisys became the owner of the Sperry patents. CompuServe saw no reason to
place any restrictions on GIF usage, and GIF graphics quickly became a staple of the World
Wide Web. They were relatively easy to create, relatively compact, and quite flexible.
Contd...

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