Huffman Encoding and Decoding for Text Files

In this project, our primary objective is to implement Huffman encoding and decoding for specified text files. To achieve this goal, we employ a memoryless source coding approach that significantly enhances encoding efficiency while maintaining data integrity during the decoding process. The implementation involves constructing optimal prefix codes based on character frequency statistics, where more frequent characters receive shorter codes and less frequent characters longer codes. The core algorithm utilizes a priority queue (typically implemented as a min-heap) to build the Huffman tree through iterative merging of nodes with the lowest frequencies. During the encoding phase, we read the input text file, calculate character frequencies, generate Huffman codes, and produce a compressed binary output along with the code mapping table. The decoding process reconstructs the original text by traversing the Huffman tree using the encoded bitstream, requiring careful handling of the tree structure serialization for proper reconstruction. Throughout the encoding and decoding procedures, we conduct detailed analysis to evaluate compression ratios, processing efficiency, and identify the strengths and limitations of this coding methodology. By exploring Huffman encoding and decoding, we gain deeper insights into data compression algorithm principles and master essential data processing skills, including bit-level manipulation and tree-based data structure operations.