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Explore the theory and applications of data compression through this comprehensive Stanford University course that addresses the critical challenge of representing the exponentially growing amounts of digital information in succinct formats. Master fundamental lossless compression techniques including entropy coding, prefix-free codes, Huffman coding, arithmetic coding, and Asymptotic Equipartition Property, while understanding how everyday tools like GZIP and BZIP2 operate. Delve into lossy compression fundamentals covering quantization, rate-distortion theory, mutual information, and transform coding as applied to real-world scenarios in image and audio processing. Examine current industry-standard compression techniques including JPEG, BPG, H264, and H265 video codecs, alongside cutting-edge machine learning approaches to image and video compression. Gain hands-on experience through practical implementation of compression algorithms using a pedagogical data compression library, reinforcing theoretical concepts through direct application. Learn from expert faculty including Professor Tsachy Weissman and explore advanced topics such as perceptual quality optimization, genomic compression, and recent research developments in the field, with opportunities to pursue specialized areas of interest through final projects and invited research talks.
