Introduction to Image Compression and Encoding Fundamentals

Every digital image we view, share, or store relies on efficient compression to save bandwidth and storage space. Understanding the underlying mathematics and algorithms of image compression is essential for computer science students, exam candidates, and software engineers. This text-based course guides you from foundational information theory to practical compression standards. You will develop a strong conceptual framework, enabling you to analyze compression ratios, evaluate quality loss, and understand how modern image formats optimize data. What you'll learn: Understand fundamental concepts of information theory, redundancy, and entropy; Analyze the differences between lossless and lossy compression techniques; Apply classic algorithms including Huffman coding, Run-Length Encoding (RLE), and LZW; Explore transform coding principles like Discrete Cosine Transform (DCT) used in JPEG; Compare modern image standards such as WebP and AVIF alongside traditional formats; Evaluate compression performance using metrics like PSNR and MSE. We begin with core terminology and the mathematical limits of compression before moving systematically through spatial and transform-based techniques. The written explanations and step-by-step mathematical walkthroughs ensure you gain a robust academic and practical foundation. This course is designed for beginners, computer science students preparing for technical exams, and self-taught developers with no prior background in signal processing. Start reading today to master the science behind digital image optimization.