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Huffman decoder


dsig = huffmandeco(comp,dict)


dsig = huffmandeco(comp,dict) decodes the numeric Huffman code vector comp using the code dictionary dict. The argument dict is an N-by-2 cell array, where N is the number of distinct possible symbols in the original signal that was encoded as comp. The first column of dict represents the distinct symbols and the second column represents the corresponding codewords. Each codeword is represented as a numeric row vector, and no codeword in dict is allowed to be the prefix of any other codeword in dict. You can generate dict using the huffmandict function and comp using the huffmanenco function. If all signal values in dict are numeric, dsig is a vector; if any signal value in dict is alphabetical, dsig is a one-dimensional cell array.


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Create unique symbols, and assign probabilities of occurrence to them.

symbols = 1:6;
p = [.5 .125 .125 .125 .0625 .0625];

Create a Huffman dictionary based on the symbols and their probabilities.

dict = huffmandict(symbols,p);

Generate a vector of random symbols.

sig = randsrc(100,1,[symbols; p]);

Encode the random symbols.

comp = huffmanenco(sig,dict);

Decode the data. Verify that the decoded data matches the original data.

dsig = huffmandeco(comp,dict);
ans =



Convert the original signal to binary, and determine its length.

binarySig = de2bi(sig);
seqLen = numel(binarySig)
seqLen =


Convert the Huffman encoded signal and determine its length.

binaryComp = de2bi(comp);
encodedLen = numel(binaryComp)
encodedLen =


The Huffman encoded data required 224 bits, which is a 25% savings over the uncoded data.

More About


[1] Sayood, Khalid, Introduction to Data Compression, San Francisco, Morgan Kaufmann, 2000.

Introduced before R2006a

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