# Huffman Compression Algorithm

In anticipation of the start of the course “Algorithms for developers” have prepared for you a translation of another useful material.

Huffman coding is a data compression algorithm that formulates the basic idea of ​​file compression. In this article, we will talk about fixed and variable length coding, uniquely decoded codes, prefix rules, and the construction of a Huffman tree.

We know that each character is stored as a sequence of 0 and 1 and takes 8 bits. This is called fixed-length encoding because each character uses the same fixed number of bits to store.

Let’s say the text is given. How can we reduce the amount of space required to store one character?

The basic idea is variable length coding. We can use the fact that some characters in the text are more common than others (see here) to develop an algorithm that will represent the same sequence of characters with fewer bits. When coding a variable length, we assign a variable number of bits to the characters, depending on the frequency of their appearance in this text. Ultimately, some characters can take up just 1 bit, and others 2 bits, 3 or more. The problem with variable length encoding is only the subsequent decoding of the sequence.

How, knowing the sequence of bits, to decode it uniquely?

Consider the line “Aabacdab”. It has 8 characters, and when encoding a fixed length, 64 bits will be needed to store it. Note that the symbol rate “A”, “b”, “c” and “D” equals 4, 2, 1, 1 respectively. Let’s try to imagine “Aabacdab” fewer bits using the fact that “A” more common than “B”, a “B” more common than C and “D”. We start with what we encode “A” with one bit equal to 0, “B” we will assign the two-bit code 11, and using the three bits 100 and 011 we encode C and “D”.

As a result, we will succeed:

 a 0 b eleven c one hundred d 011

So the line “Aabacdab” we will code as 00110100011011 (0 | 0 | 11 | 0 | 100 | 011 | 0 | 11)using the codes above. However, the main problem will be decoding. When we try to decode the string 00110100011011, we get an ambiguous result, since it can be represented as:

```0|011|0|100|011|0|11 adacdab 0|0|11|0|100|0|11|011 aabacabd 0|011|0|100|0|11|0|11 adacabab ```

etc.

To avoid this ambiguity, we must ensure that our coding satisfies a concept such as prefix rule, which in turn implies that codes can be decoded in just one unique way. A prefix rule ensures that no code is a prefix of another. By code, we mean bits used to represent a particular character. In the example above 0 Is a prefix 011that violates the prefix rule. So, if our codes satisfy the prefix rule, then we can uniquely decode (and vice versa).

Let’s review the example above. This time we will assign for characters “A”, “b”, “c” and “D” Codes that satisfy the prefix rule.

 a 0 b 10 c 110 d 111

Using this encoding, a string “Aabacdab” will be encoded as 00100100011010 (0 | 0 | 10 | 0 | 100 | 011 | 0 | 10). But 00100100011010 we can uniquely decode and return to our original line “Aabacdab”.

### Huffman coding

Now that we’ve figured out variable-length coding and a prefix rule, let’s talk about Huffman coding.

The method is based on the creation of binary trees. In it, a node can be either finite or internal. Initially, all nodes are considered leaves (leafs), which represent the symbol itself and its weight (i.e. the frequency of occurrence). Internal nodes contain the weight of the character and refer to two descendant nodes. By common agreement, a bit “0” represents the left branch, and “1” – on the right. In a full tree N leaves and N-1 internal nodes. It is recommended that when building a Huffman tree, unused characters be discarded to obtain codes of optimal length.

We will use the priority queue to build the Huffman tree, where the node with the lowest frequency will be given the highest priority. The construction steps are described below:

1. Create a leaf node for each character and add them to the priority queue.
2. While in line for more than one sheet, do the following:
• Remove the two nodes with the highest priority (with the lowest frequency) from the queue;
• Create a new internal node where these two nodes will be heirs, and the frequency of occurrence will be equal to the sum of the frequencies of these two nodes.
• Add a new node to the priority queue.
3. The only remaining node will be the root, this will finish the tree construction.

Imagine that we have some text that consists only of characters “A”, “b”, “c”, “d” and E, and their occurrence frequencies are 15, 7, 6, 6, and 5, respectively. Below are illustrations that reflect the steps of the algorithm.

The path from the root to any end node will store the optimal prefix code (also known as the Huffman code) corresponding to the character associated with that end node.

Huffman tree

Below you will find the implementation of the Huffman compression algorithm in C ++ and Java:

``````#include
#include
#include
#include
using namespace std;

// A Tree node
struct Node
{
char ch;
int freq;
Node *left, *right;
};

// Function to allocate a new tree node
Node* getNode(char ch, int freq, Node* left, Node* right)
{
Node* node = new Node();

node->ch = ch;
node->freq = freq;
node->left = left;
node->right = right;

return node;
}

// Comparison object to be used to order the heap
struct comp
{
bool operator()(Node* l, Node* r)
{
// highest priority item has lowest frequency
return l->freq > r->freq;
}
};

// traverse the Huffman Tree and store Huffman Codes
// in a map.
void encode(Node* root, string str,
unordered_map &huffmanCode)
{
if (root == nullptr)
return;

// found a leaf node
if (!root->left && !root->right) {
huffmanCode[root->ch] = str;
}

encode(root->left, str + "0", huffmanCode);
encode(root->right, str + "1", huffmanCode);
}

// traverse the Huffman Tree and decode the encoded string
void decode(Node* root, int &index, string str)
{
if (root == nullptr) {
return;
}

// found a leaf node
if (!root->left && !root->right)
{
cout << root->ch;
return;
}

index++;

if (str[index] =='0')
decode(root->left, index, str);
else
decode(root->right, index, str);
}

// Builds Huffman Tree and decode given input text
void buildHuffmanTree(string text)
{
// count frequency of appearance of each character
// and store it in a map
unordered_map freq;
for (char ch: text) {
freq[ch]++;
}

// Create a priority queue to store live nodes of
// Huffman tree;
priority_queue, comp> pq;

// Create a leaf node for each character and add it
// to the priority queue.
for (auto pair: freq) {
pq.push(getNode(pair.first, pair.second, nullptr, nullptr));
}

// do till there is more than one node in the queue
while (pq.size() != 1)
{
// Remove the two nodes of highest priority
// (lowest frequency) from the queue
Node *left = pq.top(); pq.pop();
Node *right = pq.top();	pq.pop();

// Create a new internal node with these two nodes
// as children and with frequency equal to the sum
// of the two nodes' frequencies. Add the new node
// to the priority queue.
int sum = left->freq + right->freq;
pq.push(getNode('', sum, left, right));
}

// root stores pointer to root of Huffman Tree
Node* root = pq.top();

// traverse the Huffman Tree and store Huffman Codes
// in a map. Also prints them
unordered_map huffmanCode;
encode(root, "", huffmanCode);

cout << "Huffman Codes are :n" << 'n';
for (auto pair: huffmanCode) {
cout << pair.first << " " << pair.second << 'n';
}

cout << "nOriginal string was :n" << text << 'n';

// print encoded string
string str = "";
for (char ch: text) {
str += huffmanCode[ch];
}

cout << "nEncoded string is :n" << str << 'n';

// traverse the Huffman Tree again and this time
// decode the encoded string
int index = -1;
cout << "nDecoded string is: n";
while (index < (int)str.size() - 2) {
decode(root, index, str);
}
}

// Huffman coding algorithm
int main()
{
string text = "Huffman coding is a data compression algorithm.";

buildHuffmanTree(text);

return 0;
}``````
``````import java.util.HashMap;
import java.util.Map;
import java.util.PriorityQueue;

// A Tree node
class Node
{
char ch;
int freq;
Node left = null, right = null;

Node(char ch, int freq)
{
this.ch = ch;
this.freq = freq;
}

public Node(char ch, int freq, Node left, Node right) {
this.ch = ch;
this.freq = freq;
this.left = left;
this.right = right;
}
};

class Huffman
{
// traverse the Huffman Tree and store Huffman Codes
// in a map.
public static void encode(Node root, String str,
Map huffmanCode)
{
if (root == null)
return;

// found a leaf node
if (root.left == null && root.right == null) {
huffmanCode.put(root.ch, str);
}

encode(root.left, str + "0", huffmanCode);
encode(root.right, str + "1", huffmanCode);
}

// traverse the Huffman Tree and decode the encoded string
public static int decode(Node root, int index, StringBuilder sb)
{
if (root == null)
return index;

// found a leaf node
if (root.left == null && root.right == null)
{
System.out.print(root.ch);
return index;
}

index++;

if (sb.charAt(index) == '0')
index = decode(root.left, index, sb);
else
index = decode(root.right, index, sb);

return index;
}

// Builds Huffman Tree and huffmanCode and decode given input text
public static void buildHuffmanTree(String text)
{
// count frequency of appearance of each character
// and store it in a map
Map freq = new HashMap<>();
for (int i = 0 ; i < text.length(); i++) {
if (!freq.containsKey(text.charAt(i))) {
freq.put(text.charAt(i), 0);
}
freq.put(text.charAt(i), freq.get(text.charAt(i)) + 1);
}

// Create a priority queue to store live nodes of Huffman tree
// Notice that highest priority item has lowest frequency
PriorityQueue pq = new PriorityQueue<>(
(l, r) -> l.freq - r.freq);

// Create a leaf node for each character and add it
// to the priority queue.
for (Map.Entry entry : freq.entrySet()) {
}

// do till there is more than one node in the queue
while (pq.size() != 1)
{
// Remove the two nodes of highest priority
// (lowest frequency) from the queue
Node left = pq.poll();
Node right = pq.poll();

// Create a new internal node with these two nodes as children
// and with frequency equal to the sum of the two nodes
// frequencies. Add the new node to the priority queue.
int sum = left.freq + right.freq;
}

// root stores pointer to root of Huffman Tree
Node root = pq.peek();

// traverse the Huffman tree and store the Huffman codes in a map
Map huffmanCode = new HashMap<>();
encode(root, "", huffmanCode);

// print the Huffman codes
System.out.println("Huffman Codes are :n");
for (Map.Entry entry : huffmanCode.entrySet()) {
System.out.println(entry.getKey() + " " + entry.getValue());
}

System.out.println("nOriginal string was :n" + text);

// print encoded string
StringBuilder sb = new StringBuilder();
for (int i = 0 ; i < text.length(); i++) {
sb.append(huffmanCode.get(text.charAt(i)));
}

System.out.println("nEncoded string is :n" + sb);

// traverse the Huffman Tree again and this time
// decode the encoded string
int index = -1;
System.out.println("nDecoded string is: n");
while (index < sb.length() - 2) {
index = decode(root, index, sb);
}
}

public static void main(String[] args)
{
String text = "Huffman coding is a data compression algorithm.";

buildHuffmanTree(text);
}
}``````

Note: the memory used by the input string is 47 * 8 = 376 bits, and the encoded string takes only 194 bits, i.e. data is compressed by about 48%. In the C ++ program above, we use the string class to store the encoded string to make the program readable.

Because efficient priority queue data structures require insertion O (log (N)) time, and in a full binary tree with N leaves present 2N-1 nodes, and the Huffman tree is a complete binary tree, then the algorithm works for O (Nlog (N)) time where N - Characters.