Shannon's noiseless coding theorem

In information theory, Shannon's noiseless coding theorem places an upper and a lower bound on the minimal possible expected length of codewords as a function of the entropy of the input word (which is viewed as a random variable) and of the size of the target alphabet.

Shannon's statement

Let X be a random variable taking values in some finite alphabet $\Sigma _{1}$ and let f be a decipherable code from $\Sigma _{1}$ to $\Sigma _{2}$ where $|\Sigma _{2}^{*}|=a$. Let S denote the resulting wordlength of f(X).

If f is optimal in the sense that it has the minimal expected wordlength for X, then

${\frac {H(X)}{\log _{2}a}}\leq \mathbb {E} S<{\frac {H(X)}{\log _{2}a}}+1$

Proof

Let $s_{i}$ denote the wordlength of each possible $x_{i}$ ($i=1,\ldots ,n$). Define $q_{i}=a^{-s_{i}}/C$, where C is chosen so that $\sum q_{i}=1$.

Then

${\begin{matrix}H(X)&=&-\sum _{i=1}^{n}p_{i}\log _{2}p_{i}\\&&\\&\leq &-\sum _{i=1}^{n}p_{i}\log _{2}q_{i}\\&&\\&=&-\sum _{i=1}^{n}p_{i}\log _{2}a^{-s_{i}}+\sum _{i=1}^{n}\log _{2}C\\&&\\&\leq &-\sum _{i=1}^{n}-s_{i}p_{i}\log _{2}a\\&&\\&\leq &-\mathbb {E} S\log _{2}a\\\end{matrix}}$

where the second line follows from Gibbs' inequality and the third line follows from Kraft's inequality: $C=\sum _{i=1}^{n}a^{-s_{i}}\leq 1$ so $\log C\leq 0$.

For the second inequality we may set

$s_{i}=\lceil -\log _{a}p_{i}\rceil$

so that

$-\log _{a}p_{i}\leq s_{i}<-\log _{a}p_{i}+1$

and so

$a^{-s_{i}}\leq p_{i}$

and

$\sum a^{-s_{i}}\leq \sum p_{i}=1$

and so by Kraft's inequality there exists a prefix-free code having those wordlengths. Thus the minimal S satisfies

${\begin{matrix}\mathbb {E} S&=&\sum p_{i}s_{i}\\&&\\&<&\sum p_{i}\left(-\log _{a}p_{i}+1\right)\\&&\\&=&\sum -p_{i}{\frac {\log _{2}p_{i}}{\log _{2}a}}+1\\&&\\&=&{\frac {H(X)}{\log _{2}a}}+1\\\end{matrix}}$

See Also

• Source coding