Huffman Coding Techniques in Digital Image Processing

In digital image processing, encoding techniques play a crucial role. This program implements Huffman coding for input images, demonstrating the original image's entropy and average code word length after encoding, while also capable of reconstructing images from the encoded data. The implementation uses C++/MATLAB hybrid programming with pre-compiled MATLAB library files, eliminating the need for additional C++ linking. The package includes complete source code, original test images, and resulting encoded images. Additionally, users can modify encoding parameters to conduct image coding experiments and further optimize encoding performance through parameter tuning. Key implementation details: - The Huffman coding algorithm builds optimal prefix codes based on pixel frequency distribution - Entropy calculation uses the standard Shannon entropy formula for image data - Image reconstruction validates the lossless nature of the encoding process - The hybrid architecture leverages C++ for computational efficiency while maintaining MATLAB's user interface advantages