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To display images correctly the gamma values should be used for a correction of the image.
Some systems, for example MACs, allow to store a gamma value as system parameter everything
displayed will be shown gamma corrected. With PCs, calibration tools could be used to
set the system gamma. If no gamma correction is applied, images may look washed out or too
dark.
Even with gamma correction, there may still be a problem: RGB = (255, 0, 0) just says that the
pixel will be pure red, but what this is may differ from screen to screen. Good monitors provide
the possibility to adjust the color temperature so that some compensation is possible. The
color temperature is given in Kelvin. Common color temperatures are between 5000 K and
9500 K. With a higher temperature white looks more blue, with a lower temperature more
reddish. Modern computer monitors usually allow to store different modes that can be use for
different applications. A complete solution however needs a calibration tool.
2.4 Compression
Methods to reduce storage space are essential for an image database on the Web, because images
occupy large amounts of hard disc space and network bandwidth is limited. The basic idea
in compression is to reduce redundancy present in images, and if lossy, to throw away the least
important data.
Usually „lossless“ and „lossy“ compression algorithms are distinguished. Lossless compression
algorithms allow to rebuild the original data file from a compressed one, whereas lossy algorithms
only allow to compute an approximation of the original. For many applications like
word processors and source code only lossless compression techniques may be used. However,
in the area of sound, video, and imaging, lossy compression algorithms are very important,
especially considering that a digital image is usually only an approximation of the reality due to
the analog/digital conversion.
There are a few facts about compression everybody should know:
• There is no lossless algorithm that compresses everything.
• Compressing the „same“ image in a higher resolution will usually lead to a higher compression
factor as redundancy is increased.
• As a rule of thumb: Lossless compression factors for images ≤ 1:2.5
• As a rule of thumb: Lossy compression factors for images: 1:10, 1:30 and higher
It can be shown with a simple proof [5] that there cannot be any lossless algorithm that compresses
all files larger than a given size N: Lets assume there was such a program. This implies
that each file of length N has to be mapped to a different file with length smaller N. There are
2 N files of length N but only 2 N -1 of length smaller N. So there cannot be a bije ction.
Increasing the resolution will create additional pixels that usually will have a similar color as
the pixels nearby. Due to this only little information is added, the redundancy increased and a
higher compression ratio achieved.
Of course there are special applications classes that compress much better than 1:2.5, for example
constructed images like presentation slides, or postscript files, which may be compressed
by 1:10 or better. However, the average photographic image will only compress lossless with a
factor of 2.5 or less. With our dermatologic images we got factors of about 1.5 with Lempel-
Ziv-Welch (see below) compression and about 2.2 with the compression implemented in PNG
(see below).
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