Two-Dimensional Image Compression Using Discrete Cosine Transform

This experiment involves compressing two-dimensional images using MATLAB programming. We will employ Discrete Cosine Transform (DCT) technology to achieve image compression. DCT converts image data into frequency domain representations, effectively reducing storage requirements and transmission bandwidth. The MATLAB implementation will include: - Utilizing built-in functions like dct2() for forward DCT transformation and idct2() for inverse transformation - Implementing quantization matrices to control compression ratios by discarding high-frequency coefficients - Calculating compression rates by comparing original and compressed file sizes - Evaluating image quality using metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) During the experiment, we will investigate how different DCT parameters (including block size selection and quantization thresholds) impact both image quality and compression ratios. The analysis will involve comparing visual results and numerical data to determine optimal parameter configurations. This practical approach will provide deep insights into fundamental image compression principles while developing proficiency in MATLAB-based image processing and algorithmic implementation. The experimental framework will demonstrate practical trade-offs between compression efficiency and visual fidelity in digital image processing.