Difference between revisions of "Shannon's source coding theorem"
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* [[Asymptotic Equipartition Property]] (AEP) | * [[Asymptotic Equipartition Property]] (AEP) | ||
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* [[Claude Shannon]] | * [[Claude Shannon]] | ||
* [[Error exponent]] | * [[Error exponent]] | ||
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* [[Noisy Channel Coding Theorem]] | * [[Noisy Channel Coding Theorem]] | ||
* [[Random variable]] | * [[Random variable]] | ||
Latest revision as of 13:04, 19 September 2016
In information theory, Shannon's source coding theorem (or noiseless coding theorem) establishes the limits to possible data compression, and the operational meaning of the Shannon entropy.
Description
The source coding theorem shows that (in the limit, as the length of a stream of independent and identically-distributed random variable (i.i.d.) data tends to infinity) it is impossible to compress the data such that the code rate (average number of bits per symbol) is less than the Shannon entropy of the source, without it being virtually certain that information will be lost.
However it is possible to get the code rate arbitrarily close to the Shannon entropy, with negligible probability of loss.
The source coding theorem for symbol codes 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.
See also
- Asymptotic Equipartition Property (AEP)
- Claude Shannon
- Error exponent
- Forward error correction
- Noisy Channel Coding Theorem
- Random variable
External links
- Shannon's source coding theorem @ Wikipedia
