truncated binary encoding

Truncated binary encoding is an entropy encoding typically used for uniform probability distributions with a finite alphabet. It is parameterized by an alphabet with total size of number ''n''. It is a slightly more general form of binary encoding when ''n'' is not a power of two.

If ''n'' is a power of two, then the coded value for 0 ≤ ''x'' < ''n'' is the simple binary code for ''x'' of length log₂(''n''). Otherwise let ''k'' = floor(log₂(''n'')), such that 2^''k'' < ''n'' < 2^''k''+1and let ''u'' = 2^''k''+1 − ''n''.

Truncated binary encoding assigns the first ''u'' symbols codewords of length ''k'' and then assigns the remaining ''n'' − ''u'' symbols the last ''n'' − ''u'' codewords of length ''k'' + 1. Because all the codewords of length ''k'' + 1 consist of an unassigned codeword of length ''k'' with a "0" or "1" appended, the resulting code is a prefix code.

History

Used since at least 1984, phase-in codes, also known as economy codes,Job van der Zwan
"Phase-in Codes"
are also known as truncated binary encoding.

Example with ''n'' = 5

For example, for the alphabet , ''n'' = 5 and 2² ≤ ''n'' < 2³, hence ''k'' = 2 and ''u'' = 2³ − 5 = 3. Truncated binary encoding assigns the first ''u'' symbols the codewords 00, 01, and 10, all of length 2, then assigns the last ''n'' − ''u'' symbols the codewords 110 and 111, the last two codewords of length 3.

For example, if ''n'' is 5, plain binary encoding and truncated binary encoding allocates the following codewords. Digits shown struck are not transmitted in truncated binary.

It takes 3 bits to encode ''n'' using straightforward binary encoding, hence 2³ − ''n'' = 8 − 5 = 3 are unused.

In numerical terms, to send a value ''x'', where 0 ≤ ''x'' < ''n'', and where there are 2^''k'' ≤ ''n'' < 2^''k''+1 symbols, there are ''u'' = 2^''k''+1 − ''n'' unused entries when the alphabet size is rounded up to the nearest power of two. The process to encode the number ''x'' in truncated binary is: if ''x'' is less than ''u'', encode it in ''k'' binary bits; if ''x'' is greater than or equal to ''u'', encode the value ''x'' + ''u'' in ''k'' + 1 binary bits.

Example with ''n'' = 10

Another example, encoding an alphabet of size 10 (between 0 and 9) requires 4 bits, but there are 2⁴ − 10 = 6 unused codes, so input values less than 6 have the first bit discarded, while input values greater than or equal to 6 are offset by 6 to the end of the binary space. (Unused patterns are not shown in this table.)

To decode, read the first ''k'' bits. If they encode a value less than ''u'', decoding is complete. Otherwise, read an additional bit and subtract ''u'' from the result.

Example with ''n'' = 7

Here is a more extreme case: with ''n'' = 7 the next power of 2 is 8, so ''k'' = 2 and ''u'' = 2³ − 7 = 1:

This last example demonstrates that a leading zero bit does not always indicate a short code; if ''u'' < 2^''k'', some long codes will begin with a zero bit.

Simple algorithm

Generate the truncated binary encoding for a value ''x'', 0 ≤ ''x'' < ''n'', where ''n'' > 0 is the size of the alphabet containing ''x''. ''n'' need not be a power of two.

string TruncatedBinary (int x, int n)

The routine Binary is expository; usually just the rightmost len bits of the variable ''x'' are desired.
Here we simply output the binary code for ''x'' using len bits, padding with high-order 0s if necessary.

string Binary (int x, int len)

On efficiency

If ''n'' is not a power of two, and ''k''-bit symbols are observed with probability ''p'', then (''k'' + 1)-bit symbols are observed with probability 1 − ''p''. We can calculate the expected number of bits per symbol $b_e$ as

: $b_e = p k + (1 - p) (k + 1).$

Raw encoding of the symbol has $b_u = k + 1$ bits. Then relative space saving ''s'' (see Data compression ratio) of the encoding can be defined as

: $s = 1 - \frac = 1 - \frac.$

When simplified, this expression leads to

: $s = \frac = \frac.$

This indicates that relative efficiency of truncated binary encoding increases as probability ''p'' of ''k''-bit symbols increases, and the raw-encoding symbol bit-length $b_u$ decreases.

See also

* Benford's law
* Golomb coding

References

{{DEFAULTSORT:Truncated Binary Encoding
Entropy coding
Lossless compression algorithms