MATLAB Implementation of Arithmetic Coding for Image Compression Algorithms

This project presents MATLAB-based implementations of image compression algorithms, featuring complete source code for various compression techniques including Huffman coding, arithmetic coding, DCT transformation, and wavelet transforms. The implementation includes core functions for encoding and decoding processes, with MATLAB's built-in matrix operations efficiently handling transformation algorithms like DCT and wavelet decomposition. Additionally, the content covers fundamental concepts of image compression algorithms, explaining the differences between lossless and lossy compression methods. It discusses common image file formats (such as JPEG, PNG) and their characteristics, including how different algorithms are applied in these formats. The analysis includes comparative advantages and disadvantages of various techniques - for instance, Huffman coding's high compression ratio versus its higher computational complexity in encoding/decoding processes, and the crucial role of DCT transformation in JPEG compression standards. The MATLAB implementations demonstrate practical applications through well-structured code modules that handle probability modeling in arithmetic coding, frequency analysis in Huffman coding, and transformation matrix operations in DCT implementations. Each algorithm section includes performance analysis code that calculates compression ratios and quality metrics, helping users evaluate different methods. Through these comprehensive implementations and supplementary explanations, readers can gain a thorough understanding of image compression algorithm principles, practical MATLAB coding techniques, and real-world application scenarios. The code organization follows modular design principles, allowing easy integration and modification of individual compression components.