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Lossy compression throws away information. Due to this, much higher compression ratios can
be achieved compared to lossless compression. Lossy compression techniques have been designed
for the human observer. Usually there is no visible difference between original and
compressed image at a compression factor of 1:10 for printed images and 1:30 for monitor
images. Although there is no difference for a human observer lossy compressed images usually
cannot be used for pattern analysis purposes.
2.4.1 Techniques
Many different techniques are used when compressing images. A selection is described in
short, to give an impression of how compression works:
• Reduction of color depth is a simple way to achieve compression, for example storing an
image with a color palette of 256 colors reduces the size of a true color image nearly to one
third. This is a simple form of scalar vector quantization.
• Conversion to a color system that uses full luminance data and reduces the chrominance
data. This works because the human eye is not as sensitive to color information as it is to
luminance.
• Run Length Encoding (RLE) stores the number of pixels of one color and the color instead
of storing the color for every pixel. Not suitable for photographic images, but may help with
computer generated slides and screen shots.
• Difference encoding: Instead of storing the value for one bit the difference to the value of
the neighbor is stored using fewer bits. Applied in many compression algorithms.
• Prediction: The value of the neighbors is used to predict the value of a pixel. Stored is usually
the difference to the predicted value. Very useful in lossless compression.
• Filtering: Some manipulation is done with the data to allow for better compression with
another technique, e.g. using difference encoding and prediction techniques
• Lempel-Ziv-Welch: Works similar to RLE, but uses patterns together with a lookup table
instead of bytes [6]. Works very well for text.
• Entropy encoding: This is closely related to the Huffman code [7]. The most frequent used
terms of a given alphabet will be assigned the shortest codes. Arithmetic coding is an improvement
of Huffman coding.
• Pyramids: The image (or a transformation of this image) is decomposed the in a tree like
manner. Nodes will only be further decomposed until they consist of a single value.
• Discrete Cosine Transform(ation) (DCT): Is a relative of the Fourier transform and likewise
gives a frequency map. This technique is lossy due to roundoff errors.
• Fractal: A system of iterated functions is selected for the description of the image. Offers
good compression; scaling is possible.
• Discrete Wavelet Transform (DWT): Uses Haar-functions to code images. Is one of the
most promising techniques today. Our experiments have shown better compression rations
compared to DCT because the distortions look more natural in current implementations.
2.5 File Formats
Different frequently used file formats are introduced:
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