Image Compression Fundamentals for Electronics and Communication

Preparing for an electronics and communication engineering exam on image processing, or looking to build a rock-solid understanding of digital media transmission? Grasping the mathematical and algorithmic foundations of image compression is essential for modern engineering students. This text-only course guides you step-by-step through the core concepts of image compression, breaking down complex mathematical transforms and coding algorithms into clear, readable explanations. You will transition from basic signal theory to understanding how modern codecs achieve high-fidelity compression. In this course, you will: Learn to understand the fundamental principles of redundancy, information theory, and entropy in digital images; Apply lossless compression techniques including Huffman coding, Run-Length Encoding, and LZW; Analyze lossy compression frameworks, focusing on Discrete Cosine Transform and quantization; Explore standard architectures such as JPEG and compare them with modern formats like WebP; Practice solving typical university exam-style problems on coding efficiency and compression ratios. The course begins with foundational definitions of pixel redundancy and information theory, then progresses systematically through lossless and lossy algorithms, concluding with practical engineering calculations and modern standards. This course is designed for undergraduate electronics, communication, and computer science students preparing for exams, as well as self-taught programmers seeking a theoretical foundation in signal processing. No prior image processing experience is required. Start reading today to master the core principles of image compression and confidently approach your engineering exams.