In the field of data compression, Shannon-Fano coding is a suboptimal technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured).

**Techniques
used for compression of information.**

**Shannon Fano Coding
Techniques**

In
the field of data compression, **Shannon-Fano coding** is a suboptimal
technique for constructing a prefix code based on a set of symbols and their
probabilities (estimated or measured).

In
Shannon-Fano coding, the symbols are arranged in order from most probable to
least probable, and then divided into two sets whose total probabilities are as
close as possible to being equal.All symbols then have the first digits of
their codes assigned; symbols in the first set receive "0" and
symbols in the second set receive "1". As long as any sets with more
than one member remain, the same process is repeated on those sets, to
determine successive digits of their codes. When a set has been reduced to one
symbol, of course, this means the symbol's code is complete and will not form
the prefix of any other symbol's code.

The
algorithm works, and it produces fairly efficient variable-length encodings;
when the two smaller sets produced by a partitioning are in fact of equal
probability, the one bit of information used to distinguish them is used most
efficiently. Unfortunately, Shannon-Fano does not always produce optimal prefix
codes; the set of probabilities {0.35, 0.17, 0.17, 0.16, 0.15} is an example of
one that will be assigned

**Shannon-Fano Algorithm**

A
Shannon-Fano tree is built according to a specification designed to define an
effective code table. The actual algorithm is simple:

1.
For a given list of symbols, develop a
corresponding list of probabilities or frequency counts so that each symbol‘
known.

2.
Sort the lists of symbols according to
frequency, with the most frequently occurring symbols at the left and the least
common at the right.

1.
Divide the list into two parts, with the
total frequency counts of the left half being as close to the total of the
right as possible

2.
The left half of the list is assigned
the binary digit 0, and the right half is assigned the digit 1. This means that
the codes for the symbols in the first half will all start with 0, and the
codes in the second half will all start with 1.

3.
Recursively apply the steps 3 and 4 to
each of the two halves, subdividing groups and adding bits to the codes until
each symbol has become a corresponding code leaf on the tree.

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Analog and Digital Communication : Techniques used for compression of information |

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