MATLAB Programming Implementation of JPEG Compression and Encoding

Implementing JPEG image compression and encoding using MATLAB programming is both engaging and challenging. In this project, we explore the principles of JPEG compression algorithms and develop MATLAB code to execute the entire process. The implementation involves several key stages: image sampling, Discrete Cosine Transform (DCT) for frequency domain conversion, and quantization to reduce data precision. Through these operations, we significantly decrease image file size and storage requirements while maintaining acceptable visual quality. The coding phase incorporates Huffman encoding to further optimize data storage efficiency by assigning shorter codes to more frequent data patterns. This project provides hands-on experience with critical image processing techniques, including color space conversion (RGB to YCbCr), block processing using 8x8 pixel matrices, and quantization table optimization. Key MATLAB functions employed include dct2() for 2D discrete cosine transform, quantization matrix operations, and custom Huffman coding implementation. Completing this project deepens understanding of JPEG compression mechanics while enhancing practical MATLAB programming skills for digital image processing applications.