MATLAB Implementation of Discrete Cosine Transform Compression Program

In this article, I'd like to share my experience implementing a compression program using Discrete Cosine Transform (DCT) in MATLAB. During my digital signal processing course, I studied image compression methods and recognized the significance of DCT in image compression applications. We decided to create a practical implementation using MATLAB programming. Our implementation involved writing MATLAB code that performs DCT-based compression through several key steps: first loading and preprocessing the image data, then applying block-based DCT transformation using MATLAB's dct2 function, implementing quantization with adjustable compression ratios, and finally reconstructing the image through inverse DCT. We tested various parameters and variables to examine different scenarios and outcomes, making appropriate adjustments to optimize compression quality and efficiency. Through this project, I gained practical knowledge in MATLAB programming and deepened my understanding of DCT applications in image compression. The implementation helped me appreciate both the practical applications and challenges in digital signal processing. If you're interested in this topic, I encourage you to try implementing your own version to better understand the process and expand your knowledge of digital signal processing techniques. Key implementation details include: - Using 8x8 pixel blocks for DCT transformation - Implementing customizable quantization matrices for compression control - Handling both grayscale and color images with proper channel processing - Calculating compression ratios and quality metrics like PSNR for evaluation