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can easily calculate where each record starts and thus
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randomly skip to any record in the file. THE MANAGER, and
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other database programs pad their records with spaces. In
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either case there is a great deal of space to be gained when
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packing this type of file. We have seen some DBASE III files
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in excess of 1 megabyte 'crunch' down to only 50,000
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characters or so. Most of this is due to packing.
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ARC VERSION 2.20 PAGE - 29
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There is one slight problem with this method. Suppose
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you are using a zero-byte as the control character. If a
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sequence of only one zero is encountered, you cannot code it
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to the output file since it will be interpreted as a control
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character. You must send a three byte control sequence to
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code the single zero. An example of this would be as
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follows:
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.:0801 06 08 01 00 8f 00 0c 08
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.:0809 02 00 8f 00 12 08 03 00
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.:0811 8f 00 00 00 00 00 00 00 and so on....
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This would be stored on disk as the sequence:
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06 08 01 00 00 01 8f 00 00 01 0c 08 02 00 00 01
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8f 00 00 01 12 08 03 00 00 01 8f 00 00 07 .....
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We went from 24 bytes to 30! Not much of a savings.
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It is because of this problem with packing that
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squeezed files are occasionally shorter than their squashed
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equivalent.
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ARC VERSION 2.20 PAGE - 30
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Huffman coding is somewhat more complex. It is probably
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the most elegant of all the compression methods used and
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certainly the most common. It takes advantage of the fact
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that some characters are used more often than others in
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most files. Text files contain many spaces, and letters
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like a,e or c are much more common than x, z, or q. Graphics
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files contain many zeros or $ff's and machine language
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programs tend to contain more LDA's and STA's than EOR's or
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ROR's.
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Suppose now that a file contains only the characters a
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through z. Since there are only 26 characters used, a five
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bit binary number, which can take on 32 possible values,
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would be more than adequate to represent each character. We
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could assign a five bit number to each of the characters a
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to z and gain 3 bits per character or 37.5%
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Huffman coding takes this one step further.
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Suppose also that some of the characters occur much
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more often in the file than do others. We could gain even
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more space if the frequently occurring characters were
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assigned codes less than five bits long, and the less
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frequently occurring characters were assigned codes that
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were five or more bits long. The Huffman algorithm does just
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that.
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The Huffman algorithm converts fixed length codes (8
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bit characters) into codes whose length in bits is inversely
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proportional to their probability of occurrence in the data
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file. For example, suppose your data file looked something
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like this:
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ARC VERSION 2.20 PAGE - 31
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abracadabra
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The character frequency distribution is as follows:
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total bits total bits
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character frequency huffman code unsqueezed squeezed
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--------- --------- ------------ ---------- -----------
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a 5 0 8 * 5 = 40 1 * 5 = 5
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b 2 10 8 * 2 = 16 2 * 2 = 4
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