To compress the string with no loss of data (lossless). For more such technical knowledge, visit our blogs at Favtutor.Instructions you will be generating Huffman codes toĬompress a given string. It is highly recommended to understand the working of Huffman coding and use it to compress your data efficiently. It is one of the best ways to compress the data which losing it and transfer data over the network efficiently. Huffman coding is one of the greedy algorithms widely used by programmers all over the world. Huffman coding is used by multimedia codecs like JPEG, PNG, MP3, etc.It is used for text and fax transmission.Huffman coding is used for conventional compression formats like GZIP, etc.It is because the encoding of the text is depended on the frequency of the characters. The time complexity of Huffman coding is O(n logn), where n is the number of unique characters. items(), key = lambda x: x, reverse = True) Nodes = sorted(nodes, key = lambda x: x, reverse = True)įreq = sorted(freq. ''' Function to make tree :param nodes: Nodes :return: Root of the tree ''' while len(nodes) > 1: 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. For example, if you wish to decode 01, we traverse from the root node as shown in the below image.ĭef _init_( self, left = None, right = None):ĭef huffman_code_tree(node, binString = ''): 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 by 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. 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 with the largest code. The basic idea behind the Huffman coding algorithm is to assign the variable-length codes to input characters of text based on the frequencies of the corresponding character. Therefore, a 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. So, let's get started! What is Huffman Coding? In this article, we will study Huffman coding, and Huffman tree along with its algorithm, python code, and example is. This algorithm is commonly found in almost all programming languages like C, C++, Java, Python, JavaScript, etc. 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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