MATLAB Implementation of Shannon-Fano Encoding and Decoding for Image Compression

This MATLAB program implements Shannon-Fano encoding and decoding algorithms for efficient compression and decompression of both binary and color images. The Shannon-Fano coding method is a widely-used data compression algorithm that employs variable-length codes to reduce data storage requirements. The implementation includes key functions for calculating symbol probabilities, sorting symbols in descending probability order, and recursively splitting symbol sets to build optimal prefix codes. The program can process various image types including grayscale and color images by treating pixel values as symbols for entropy encoding. The compression module analyzes image data statistics, constructs Shannon-Fano codes based on probability distributions, and generates compressed bitstreams with corresponding code dictionaries. The decompression module reconstructs images by parsing the encoded bitstream using the stored codebook, ensuring lossless recovery of original image quality. Designed with user accessibility in mind, the program features clear function modularization with intuitive parameters, making it suitable for users with varying programming experience. The main workflow includes image preprocessing, probability calculation, code tree generation, encoding/decoding operations, and compression ratio evaluation. Batch processing capabilities allow efficient handling of multiple images while maintaining configurable compression settings for different requirements.