One Pager Cheat Sheet

• The article provides a guide on the Huffman Coding Compression Algorithm, a lossless data compression technique used to store large amounts of data in smaller spaces, similar to ZIP files, by assigning shorter representations to more frequent characters.
• Huffman coding works by creating a set of variable-length codes for characters based on their frequency, employing prefix-free codes to allow for unambiguous decoding.
• To build a Huffman tree, start with a list of characters and their frequencies, represent each character as a leaf node with a frequency, sort characters by frequency using a priority queue or min-heap, and then repeatedly merge the two least frequent nodes into a parent node until only one node, the root, remains, which can be achieved in various programming languages like Python, Java, JavaScript, C++, and Go.
• Huffman coding assigns variable-length codes to input characters based on the frequency of those characters.
• The text provides code in various programming languages (Python, Java, JavaScript, C++, and Go) to recursively traverse a tree, assigning 0 for left branches and 1 for right branches, with the structure designed so that more frequent characters get shorter codes.
• This passage describes how to encode input data by encoding each character using previously assigned codes and outputting a bit sequence, using provided encoding code examples in multiple programming languages: Python, Java, JavaScript, C++, and Go.
• The provided Python, Java, JavaScript, C++, and Go code snippets illustrate how to decode a Huffman-encoded bit sequence by using a binary tree, traversing left or right based on the bit (0 or 1) and appending the character found at the leaf node to the decoded_text.
• The algorithm analysis reveals a time complexity of O(n log n) and a space complexity of O(n) due to the storage of the Huffman Tree, and the compression ratio is dependent on the repetitiveness of characters in the input data.
• The use of Canonical Huffman codes enables more efficient storage and transmission of the code table, and adaptive Huffman coding can be utilized for tree rebuilding in dynamic situations where the input data changes over time.
• Huffman coding is a method for lossless data compression that assigns the shortest codes to the most frequently used characters, and its uses include file compression and transmission protocols, but it struggles with non-repetitive data.