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COMPRESSION
Although the cost of a byte of storage has declined rapidly, and is still declining, use of data compression techniques can almost always reduce the effective cost still further by squeezing more data into the same space. Consider a text archive or collection of documents. It may be advantageous to hold it in compressed form to save space if access to a particular document is infrequent (therefore expansion of it is performed rarely) but the document may be required quickly(thus it needs to be online) Compression could also save time (and money) when data is transmitted; for example, compression of source code might reduce the number of diskettes needed to distribute software. In this chapter we examine some methods for compressing text. Lelewer and Hirschberg cover most of them, and others, in their survey.
Data compression relies on there being redundancy in the input. Random strings of characters are not compressible to any great degree, neither are object files. Natural language text is redundant in that not all text units (characters, character pairs, words) occur with equal fre¬quency. Compression tends to remove the redundancy, thus compression of a compressed file is normally not worthwhile. Usually compression without loss of information is required, that is, the input file should be exactly recoverable by application of some corresponding expansion tech¬nique. In some cases, an inexact reversal may be acceptable. For example, when expanded, a source program in a free format language may not need to have exactly the same layout as the original.
Compression is not without disadvantages: reduced redun¬dancy makes a file more vulnerable to storage and tran¬smission errors. Lelewer and Hirschberg discuss the susceptibility to error of the output of various algorithms. They note, for example, that the output from adaptive algorithms is more vulnerable than the output from stat¬ic algorithms but even when a static algorithm is used, an error can propagate through an entire file.
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