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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).
The optimal length-limited Huffman code will encode symbol i with a bit string of length h i. The canonical Huffman code can easily be constructed by a simple bottom-up greedy method, given that the h i are known, and this can be the basis for fast data compression. [2]
In order for a symbol code scheme such as the Huffman code to be decompressed, the same model that the encoding algorithm used to compress the source data must be provided to the decoding algorithm so that it can use it to decompress the encoded data. In standard Huffman coding this model takes the form of a tree of variable-length codes, with ...
Modified Huffman coding is used in fax machines to encode black-on-white images . It combines the variable-length codes of Huffman coding with the coding of repetitive data in run-length encoding . The basic Huffman coding provides a way to compress files with much repeating data, like a file containing text, where the alphabet letters are the ...
The two codes (the 288-symbol length/literal tree and the 32-symbol distance tree) are themselves encoded as canonical Huffman codes by giving the bit length of the code for each symbol. The bit lengths are themselves run-length encoded to produce as compact a representation as possible. As an alternative to including the tree representation ...
In the table below, the column "ISO 8859-1" shows how the file signature appears when interpreted as text in the common ISO 8859-1 encoding, with unprintable characters represented as the control code abbreviation or symbol, or codepage 1252 character where available, or a box otherwise. In some cases the space character is shown as ␠.
Rather than unary encoding, effectively this is an extreme form of a Huffman tree, where each code has half the probability of the previous code. Huffman-code bit lengths are required to reconstruct each of the used canonical Huffman tables. Each bit length is stored as an encoded difference against the previous-code bit length.
Huffman coding and arithmetic coding (when they can be used) give at least as good, and often better compression than any universal code.. However, universal codes are useful when Huffman coding cannot be used — for example, when one does not know the exact probability of each message, but only knows the rankings of their probabilities.