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Compression

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CHAPTER
27 Data Compression
Data transmission and storage cost money. The more information being dealt with, the more it
costs. In spite of this, most digital data are not stored in the most compact form. Rather, they
are stored in whatever way makes them easiest to use, such as: ASCII text from word processors,
binary code that can be executed on a computer, individual samples from a data acquisition
system, etc. Typically, these easy-to-use encoding methods require data files about twice as large
as actually needed to represent the information. Data compression is the general term for the
various algorithms and programs developed to address this problem. A compression program is
used to convert data from an easy-to-use format to one optimized for compactness. Likewise, an
uncompression program returns the information to its original form. We examine five techniques
for data compression in this chapter. The first three are simple encoding techniques, called: runlength,
Huffman, and delta encoding. The last two are elaborate procedures that have established
themselves as industry standards: LZW and JPEG.
Data Compression Strategies
Table 27-1 shows two different ways that data compression algorithms can be
categorized. In (a), the methods have been classified as either lossless or
lossy. A lossless technique means that the restored data file is identical to the
original. This is absolutely necessary for many types of data, for example:
executable code, word processing files, tabulated numbers, etc. You cannot
afford to misplace even a single bit of this type of information. In comparison,
data files that represent images and other acquired signals do not have to be
keep in perfect condition for storage or transmission. All real world
measurements inherently contain a certain amount of noise. If the changes
made to these signals resemble a small amount of additional noise, no harm is
done. Compression techniques that allow this type of...