Huffman coding compresses data based on character frequencies from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Huffman coding is a widely-used method for lossless data compression. The key idea behind Huffman coding is to assign variable-length codes to input characters, with shorter codes assigned to more frequent characters. By doing so, it is possible to represent the input data using fewer bits than a fixed-length encoding scheme. This results in a more efficient representation of the original data. The algorithm works by constructing a binary tree called a Huffman tree. This tree is built in a bottom-up fashion, starting with individual nodes for each input character, and then merging nodes with the lowest frequencies to create higher-level nodes. The process continues until all nodes are merged into a single root node, which represents the entire input data. During the construction of the Huffman tree, each merge operation involves combining two nodes with the lowest frequencies. A new node is created with a frequency equal to the sum of the frequencies of the two merged nodes. This process ensures that characters with higher frequencies are represented by shorter codes in the final encoding. Once the Huffman tree is constructed, the next step is to assign binary codewords to each input character. This is done by traversing the tree from the root to each leaf node, assigning 0 to left branches and 1 to right branches. The codeword for a character is then the sequence of 0s and 1s obtained by following the path from the root to the leaf node corresponding to that character. By assigning shorter codewords to more frequent characters and longer codewords to less frequent characters, Huffman coding achieves compression by reducing the average number of bits needed to represent the input data. This makes it an effective technique for reducing the size of data files without losing any information.
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