comp = huffmanenco(sig,dict)
comp = huffmanenco(sig,dict) encodes
sig using the Huffman codes described
by the code dictionary
dict. The argument
have the form of a numeric vector, numeric cell array, or alphanumeric
cell array. If
sig is a cell array, it must be
either a row or a column.
dict is an N-by-2 cell
array, where N is the number of distinct possible symbols to be encoded.
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
dict can be the prefix of any other codeword
dict. You can generate
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); isequal(sig,dsig)
ans = 1
Convert the original signal to binary, and determine its length.
binarySig = de2bi(sig); seqLen = numel(binarySig)
seqLen = 300
Convert the Huffman encoded signal and determine its length.
binaryComp = de2bi(comp); encodedLen = numel(binaryComp)
encodedLen = 224
The Huffman encoded data required 224 bits, which is a 25% savings over the uncoded data.
 Sayood, Khalid, Introduction to Data Compression, San Francisco, Morgan Kaufmann, 2000.