update(huffman_code_tree(r, binString + '1')) update(huffman_code_tree(l, binString + '0'))ĭ. ''' Function to find Huffman Code ''' if type(node) is str:ĭ. Here is the complete algorithm for huffman coding:ĭef _init_( self, left = None, right = None):ĭef huffman_code_tree(node, binString = ''): For example, if you wish to decode 01, we traverse from the root node as shown in the below image. How to decode the code?įor decoding the above code, you can traverse the given Huffman tree and find the characters according to the code. Whereas after encoding the text, the size is reduced to 24 + 11 + 16 = 51 bits. Without using the Huffman coding algorithm, the size of the text was 88 bits. Therefore, the total size is given in the table below: Remember that for sending the above text, we will send the tree along with the compressed code for easy decoding. Now, assign 0 to the left edges and 1 to the right edges of the Huffman coding tree as shown below. Repeat the same process until the complete Huffman tree is formed. The value of node ‘a’ will be the sum of both minimum frequencies and add it to the priority queue as shown in the below image. Later, we will assign the minimum frequency of the queue as the left child of node ‘a’ and the second minimum frequency as the right child of node ‘a’.

Now, we will create the Huffman tree using this priority queue. Consider these characters are stored in the priority queue as shown in the below image. Now, we will sort the frequencies string of the characters in increasing order. Looking at the text, the frequencies of the characters will be as shown in the below image. Let us see how to encode the above text using the Huffman coding algorithm: Hence, the decoding of the text is done using the same tree generated by the Huffman technique. Remember that we encode the text while sending it, and later, it is necessary to decode it. Using Huffman coding, we will compress the text to a smaller size by creating a Huffman coding tree using the character frequencies and generating the code for each character. Therefore, a total of 11x8=88 bits are required to send this input text. Let us understand how Huffman coding works with the example below:Īs the above text is of 11 characters, each character requires 8 bits. This method ensures that the final binary tree minimizes the total number of bits required to represent the symbols in the input.Īs a result, Huffman coding is regarded as a classic example of a greedy algorithm.

It works by selecting the lowest two frequency symbols/subtrees and merging them together at every step until all symbols or subtrees are merged into a single binary tree. Yes, Huffman coding is a greedy algorithm. Using this technique, Huffman coding ensures that there is no ambiguity when decoding the generated bitstream. Here, the codes assigned to the characters are termed prefix codes which means that the code assigned to one character is not the prefix of the code assigned to any other character. So, the most frequent character gets the smallest code, and the least frequent character is assigned the largest code. The idea of this algorithm is to assign variable-length codes to input characters of text based on the frequencies of the corresponding character.

The basic principle of Huffman coding is to compress and encode the text or the data depending on the frequency of the characters in the text. Huffman coding is a greedy algorithm frequently used for lossless data compression. In this article, we will study Huffman coding, example, algorithm, and its implementation using python. It is one of the most used algorithms for various purposes all over the technical domain. Huffman coding is a type of greedy algorithm developed by David A.

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