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Huffman coding is a lossless data compression algorithm that is widely used in various applications, including file compression, image compression, and video compression. It is based on the principle of variable-length encoding, where each symbol is assigned a unique binary code based on its frequency of occurrence in the input data.
The concept of Huffman coding involves constructing a binary tree called a Huffman tree, which is used to generate the variable-length codes for each symbol. The algorithm starts by analyzing the input data and determining the frequency of occurrence for each symbol. These frequencies are then used to build a priority queue or a min-heap, where each symbol is represented as a node with its frequency as the key.
The Huffman tree is constructed by repeatedly merging the two nodes with the lowest frequencies into a new node, until only one node remains. This final node represents the root of the Huffman tree. During the merging process, the left child of each node is assigned a binary digit '0', while the right child is assigned '1'. This binary digit assignment ensures that the codes generated for each symbol are uniquely decodable.
Once the Huffman tree is constructed, the next step is to assign the variable-length codes to each symbol. This is done by traversing the Huffman tree from the root to each leaf node, while keeping track of the path taken. The path to each leaf node represents the binary code for the corresponding symbol. The resulting codes are typically stored in a lookup table for efficient encoding and decoding.
Huffman coding achieves data compression by assigning shorter codes to symbols that occur more frequently and longer codes to symbols that occur less frequently. This results in a more efficient representation of the input data, as the most common symbols are represented by fewer bits. The compression ratio achieved by Huffman coding depends on the frequency distribution of symbols in the input data. If the input data has a skewed frequency distribution, where a few symbols occur very frequently and others occur rarely, Huffman coding can achieve significant compression.
During compression, the input data is encoded using the generated Huffman codes, resulting in a compressed representation. During decompression, the compressed data is decoded using the same Huffman codes, reconstructing the original input data. Since the Huffman codes are uniquely decodable, the original data can be perfectly reconstructed without any loss of information.
In summary, Huffman coding is a technique used in data compression to assign variable-length codes to symbols based on their frequency of occurrence. It achieves compression by assigning shorter codes to more frequent symbols, resulting in a more efficient representation of the data. Huffman coding is widely used in various compression algorithms and has proven to be effective in reducing the size of data without any loss of information.