JPEG Compression Implementation Using MATLAB

In this document, we demonstrate how to implement JPEG compression using MATLAB. The process begins with reading an image file using MATLAB's imread() function, which loads the image data into a matrix format. The core compression algorithm involves applying Discrete Cosine Transform (DCT) through functions like dct2(), which converts spatial domain image data into frequency domain representation. This transformation concentrates the image energy into fewer coefficients, enabling effective compression. Following the DCT processing, we utilize MATLAB's image display capabilities through imshow() function to visualize the transformed image. The compression implementation includes quantization steps where we divide DCT coefficients by a quantization matrix to reduce high-frequency components. Finally, the processed image is saved in JPG format using imwrite() function, which automatically applies Huffman encoding for additional compression. This implementation covers key JPEG compression components: color space conversion (RGB to YCbCr), block processing (typically 8x8 pixel blocks), DCT transformation, quantization, and entropy coding. Through these steps, we gain practical understanding of JPEG compression mechanics and can effectively apply these techniques to process image data while balancing quality and compression ratio.