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matlabrecursioncompressionhuffman-code
Michał Szydłowski
Michał Szydłowski

Reputation: 3419

Encoding a long signal in MATLAB using Huffman encoding

I'm working on lossless data compression in MATLAB. I wish to encode a signal of about 60000 length. Here's my code:

function sig = huffman (Y, fs)

%getting array of unique values
Z = unique (Y);

%counting occurences of each element and listing it to a new array
countElY=histc(Y,Z); %# get the count of elements
p = countElY/numel(Y); %getting the probability distribution

[dict,avglen] = huffmandict(Z,p); % Create dictionary.

comp = huffmanenco(Y,dict) % Encode the data.
dsig = huffmandeco(comp, dict) %Decode the data
sound(dsig, fs)

Problem is, for a signal of such length, I exceed the 500 recursion limit at MATLAB, and that error occurs while creating the dictionary. I have already tried to break the signal into parts, but that took hell lot of time, and for only a small part of it. Any ideas how to make it work? Apart from extending the recursion limit, which is rather pointless and time consuming?

Upvotes: 1

Views: 2326

Answers (1)

Mark Adler
Mark Adler

Reputation: 112627

First you need to determine why you think it's possible to compress the data. Is the signal smooth? Is the range limited? Is the quantization limited? What makes it compressible will determine how to compress it.

Simply applying Huffman coding to a series of real values will not compress the data, since each of the values appears once, or maybe a few appear twice. Huffman depends on taking advantage of many occurrences of the same symbol, and a bias in the frequency, where some symbols are much more common than others.

Compressing a waveform would use different approaches. First would be to convert each sample to as few bits as are significant and that cover the range of inputs. Second would be to take differences between samples or use more advanced predictors to take advantage of smoothness in the waveform (if it is smooth). Third would be to find a way to group differences to encode more likely ones in fewer bits. That last step might use Huffman coding, but only after you've constructed something that Huffman coding can take advantage of.

Upvotes: 2

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