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  2. Kraft–McMillan inequality - Wikipedia

    en.wikipedia.org/wiki/Kraft–McMillan_inequality

    Let each source symbol from the alphabet = {,, …,} be encoded into a uniquely decodable code over an alphabet of size with codeword lengths ,, …,. Then = Conversely, for a given set of natural numbers ,, …, satisfying the above inequality, there exists a uniquely decodable code over an alphabet of size with those codeword lengths.

  3. Variable-length code - Wikipedia

    en.wikipedia.org/wiki/Variable-length_code

    A code is uniquely decodable if its extension is § non-singular.Whether a given code is uniquely decodable can be decided with the Sardinas–Patterson algorithm.. The mapping = {,,} is uniquely decodable (this can be demonstrated by looking at the follow-set after each target bit string in the map, because each bitstring is terminated as soon as we see a 0 bit which cannot follow any ...

  4. Sardinas–Patterson algorithm - Wikipedia

    en.wikipedia.org/wiki/Sardinas–Patterson_algorithm

    Kraft's inequality in some cases provides a quick way to exclude the possibility that a given code is uniquely decodable. Prefix codes and block codes are important classes of codes which are uniquely decodable by definition. Timeline of information theory; Post's correspondence problem is similar, yet undecidable.

  5. Prefix code - Wikipedia

    en.wikipedia.org/wiki/Prefix_code

    For example, a code with code {9, 55} has the prefix property; a code consisting of {9, 5, 59, 55} does not, because "5" is a prefix of "59" and also of "55". A prefix code is a uniquely decodable code: given a complete and accurate sequence, a receiver can identify each word without requiring a special marker between words. However, there are ...

  6. Shannon's source coding theorem - Wikipedia

    en.wikipedia.org/wiki/Shannon's_source_coding...

    In information theory, the source coding theorem (Shannon 1948) [2] informally states that (MacKay 2003, pg. 81, [3] Cover 2006, Chapter 5 [4]): N i.i.d. random variables each with entropy H(X) can be compressed into more than N H(X) bits with negligible risk of information loss, as N → ∞; but conversely, if they are compressed into fewer than N H(X) bits it is virtually certain that ...

  7. Huffman coding - Wikipedia

    en.wikipedia.org/wiki/Huffman_coding

    Huffman tree generated from the exact frequencies of the text "this is an example of a huffman tree". Encoding the sentence with this code requires 135 (or 147) bits, as opposed to 288 (or 180) bits if 36 characters of 8 (or 5) bits were used (This assumes that the code tree structure is known to the decoder and thus does not need to be counted as part of the transmitted information).

  8. List decoding - Wikipedia

    en.wikipedia.org/wiki/List_decoding

    Algorithms developed for list decoding of several interesting code families have found interesting applications in computational complexity and the field of cryptography. Following is a sample list of applications outside of coding theory: Construction of hard-core predicates from one-way permutations. Predicting witnesses for NP-search problems.

  9. Unary coding - Wikipedia

    en.wikipedia.org/wiki/Unary_coding

    Unary coding, [nb 1] or the unary numeral system and also sometimes called thermometer code, is an entropy encoding that represents a natural number, n, with a code of length n + 1 ( or n), usually n ones followed by a zero (if natural number is understood as non-negative integer) or with n − 1 ones followed by a zero (if natural number is understood as strictly positive integer).