## What is Huffman coding in C?

Huffman coding is a lossless data compression algorithm. The idea is to assign variable-length codes to input characters, lengths of the assigned codes are based on the frequencies of corresponding characters. The most frequent character gets the smallest code and the least frequent character gets the largest code.

**What are the steps of Huffman coding?**

Huffman coding is done with the help of the following steps.

- Calculate the frequency of each character in the string.
- Sort the characters in increasing order of the frequency.
- Make each unique character as a leaf node.
- Create an empty node z .

**How do you print a Huffman tree?**

The steps to Print codes from Huffman Tree:

- Traverse the tree formed starting from the root.
- Maintain a string.
- While moving to the left child write ‘0’ to the string.
- While moving to the right child write ‘1’ to the string.
- Print the string when the leaf node is encountered.

### How do you read Huffman code?

Steps of Huffman Decoding are:

- Start from the root node.
- If the current bit in the given data is 0,then move to the left node of the tree.
- If the current bit in the given data is 1,then move to the right node of the tree.
- During the traversal if leaf node is encountered then print character of that leaf node.

**Where is data stored in Huffman coding?**

Any prefix-free binary code can be displayed or visualized as a binary tree with the encoded characters stored at the leaves. Huffman tree or Huffman coding tree defines as a full binary tree in which each leaf of the tree corresponds to a letter in the given alphabet.

**How do you calculate the number of bits in Huffman coding?**

The number of bits required to represent the Huffman coding tree is 9×8 + 9×2 = 90 bits, which can represented by 12 bytes. In other words, the last byte should contain only two useful bits. The 12 bytes are followed by the encoded text.

#### How is Huffman code length calculated?

Huffman coding Suppose that the lengths of the Huffman code are L=(l1,l2,…,ln) for a source P=(p1,p2,…,pn) where n is the size of the alphabet. Using a variable length code to the symbols, ljbits for sj, the average length of the codewords is (in bits): The entropy of the source is: As we know from Section 2.4.

**Why is Huffman coding optimal?**

Huffman coding is known to be optimal, yet its dynamic version may yield smaller compressed files. The best known bound is that the number of bits used by dynamic Huffman coding in order to encode a message of n characters is at most larger by n bits than the number of bits required by static Huffman coding.