## Data Compression

Data compression is a reduction in the number of bits needed to represent data. Compressing data can save storage capacity, speed up file transfer, and decrease costs for storage hardware and network bandwidth. Lossless compression techniques are a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).

## Huffman Coding

A Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression. This means that the codes (bit sequences) are assigned in such a way that the code assigned to one character is not the prefix of code assigned to any other character. The output from Huffman's algorithm can be viewed as a variable-length code table for encoding a source symbol (such as a character in a file). As in other entropy encoding methods, more common symbols are generally represented using fewer bits than less common symbols.

The technique works by creating a binary tree of nodes. These can be stored in a regular array, the size of which depends on the number of symbols ‘n’. Initially, all nodes are leaf nodes, which contain the symbol itself, the weight (frequency of appearance) of the symbol, and optionally, a link to a parent node which makes it easy to read the code (in reverse) starting from a leaf node. Internal nodes contain a weight(probability), links to two child nodes, and an optional link to a parent node. In our implementation, the weight is considered in terms of probability and the parent node is the composite sum of the child node probabilities.

Huffman coding occurs in stages which can be defined by the

number of stages,

N=Number of symbols,

r=number of digits used to represent 1 number of the code.

In our case, N=4,r=2 so 'n' is =2, which is the number of stages.

%Author:Prithvi Kishan

%Implementing Huffman encoding on numeric symbols using MATLAB functions.

clear all;

close all;

clc;

%% Generating numbers from 1 to 8 as symbols

symbols = 1:8;

%Each symbol has its repective probablity defined by p

prob = [.5 .125 .125 .125 .03125 .03125 .03125 .03125 ];

%Generate a binary Huffman code dictionary

hdict = huffmandict(symbols,prob);

%Generating a 100x1 inputsignal with values from 1 to 8

inputSig = randsrc(100,1,[symbols;prob]);

%% Converting the original signal to a binary, and determining the length of the binary symbols.

binarySig = de2bi(inputSig);

ip_seq_len_binary = numel(binarySig)

%% Generating a lossless, compressed ecoded signal

code = huffmanenco(inputSig,hdict);

%Converting the Huffman-encoded symbols to binary, and determine the length of the encoded binary symbols.

binaryComp = de2bi(code);

enc_seq_len_binary = numel(binaryComp)

%% Decoding the Huffman encoded code and comapring with original input

sig = huffmandeco(code,hdict);

if(isequal(inputSig,sig))

display('Huffman decoded signal and original signal are equal hence it is lossless');

end

## Shannon’s binary encoding theorem

Shannon coding is a lossless data compression technique for constructing a prefix code based on a set of symbols and their probabilities (estimated or measured). It is suboptimal in the sense that it does not achieve the lowest possible expected codeword length as Huffman coding does, and never better but sometimes equal to the Shannon–Fano coding.

The technique used is, the symbols are arranged in descending order of probability, and assigned codewords by taking the first integer greater than (done using ceil function in MATLAB) bits from the binary expansions of the cumulative probabilities which are initially multiplied by 2 and rounded off to the smallest integer lass than it( done using floor function in MATLAB).

% Topic:Lossless compression techniques using MATLAB

%Author:Prithvi Kishan

%Implementing Shannon binary encoding.

clc;

clear all;

close all;

%% Taking in symbols and their probabilities

m=input('Enter the number of message symbols : ');

code=[];

h=0;l=0;

display('Enter the probabilities in descending order');

for i=1:m

fprintf('Symbol %d probability \n',i);

p(i)=input('');

end

%% Finding alpha and code length array

%Finding each symbols' alpha values by adding probablities of previous symbol

a(1)=0;

for j=2:m;

a(j)=a(j-1)+p(j-1);

end

fprintf('\n Alpha Matrix');

display(a);

%Finding each code length

for i=1:m

n(i)= ceil(-1*(log2(p(i))));

end

fprintf('\n Code length matrix');

display(n);

%% Implementing shannons binary encoding algoritm

for i=1:m

int=a(i);

for j=1:n(i)

frac=int*2;

c=floor(frac);

frac=frac-c;

code=[code c];

int=frac;

end

fprintf('Codeword %d',i);

display(code);

code=[];

end

%% Computing Important compression algorithm parameters

%Computing average code length

fprintf('Avgerage Code Length');

for i=1:m

x=p(i)*n(i);

l=l+x;

x=p(i)*log2(1/p(i));

h=h+x;

end

display(l);

%Computing Entropy, effieciency and redundancy

fprintf('Entropy');

display(h);

fprintf('Efficiency');

display(100*h/l);

fprintf('Redundancy');

display(100-(100*h/l));

## Conclusion

Huffman coding and Shannon’s binary encoding techniques offer good information compression which is essential in this era of vast streams of information exchange to help in bandwidth conservation and to improve speeds. Shannon’s binary encoding although helps in lossless compression it had an efficiency of ~80% whereas Huffman coding has more than 95% in most scenarios and hence is not the optimal compression algorithm. Newer algorithms like LZ77, LZ78, LZR, etc have been developed that surpasses the efficiency of the above-mentioned algorithms.**Did you find some helpful content from our video or article and now looking for its code, model, or application? You can ****purchase**** the specific Title, if available, and instantly get the download link.**

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