JPEG Codec Implementation in MATLAB

This document describes using the JPEG MATLAB toolbox to implement JPEG encoding and decoding for experimental purposes. The implementation typically involves key functions such as dct2() for Discrete Cosine Transform, quantization with custom matrices, and entropy encoding using Huffman coding. We can extend the experimentation by comparing JPEG with other image codec algorithms like PNG (using lossless compression with DEFLATE algorithm) or GIF (employing LZW compression) to analyze their performance differences in terms of compression ratio and image quality. During experiments, we can adjust various parameters including quantization scales (affecting compression ratio from 10:1 to 20:1) and quality factors (ranging from 1 to 100) to observe their impact on encoding and decoding outcomes. Furthermore, we can explore JPEG algorithm principles such as color space conversion (RGB to YCbCr), 8x8 block processing, and optimization methods like progressive JPEG encoding. Potential improvements include implementing optimized quantization tables, developing faster DCT algorithms using butterfly structures, and incorporating error resilience features. The experimental framework allows detailed investigation of JPEG codec implementation details, including file I/O operations using imread()/imwrite(), transformation processes, and compression statistics analysis. Through systematic research and experimentation, we can gain comprehensive understanding of JPEG codec principles, MATLAB implementation techniques, and performance optimization strategies.