May 1, 2024
Updated May 9, 2025
31 minute read
At its core, compression is the process of reducing the size of data. Think of it as a way to make files smaller so they take up less storage space or can be sent more quickly over the internet. This is achieved by encoding information using fewer bits than the original representation. You encounter compression every day, often without realizing it – when you download a software update, stream a movie, or open a zipped folder on your computer. The ability to efficiently manage and transmit vast amounts of data is a cornerstone of our digital world, and compression techniques make this possible.
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Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Compression.
This practical guide focuses on implementing data compression algorithms in real-world applications. It covers various compression techniques, including Huffman coding, Lempel-Ziv-Welch (LZW), and JPEG, and provides code examples in C and Java.
This classic textbook provides a rigorous mathematical treatment of information theory and its applications in machine learning. It includes a chapter on lossless data compression, which discusses the theoretical limits of compression.
This textbook focuses on the compression of multimedia data, including images, audio, and video. It covers both theoretical concepts and practical implementation techniques, and discusses the latest compression standards such as JPEG 2000, MPEG-4, and H.264.
This textbook provides a comprehensive overview of multimedia technologies, including audio and video compression. It covers both the theoretical foundations and practical implementation of various compression algorithms, and discusses the latest developments in multimedia compression standards.
This textbook covers a broad range of data science topics, including data compression. It provides a concise overview of the fundamental concepts of lossless and lossy compression, and discusses practical applications in data mining and machine learning.
This hands-on textbook introduces data science concepts through hands-on Python coding. It covers a broad range of topics, including data compression, and provides practical examples and exercises.
This textbook provides a comprehensive overview of machine learning from a theoretical perspective. It includes a chapter on algorithmic compression, which discusses the use of compression techniques in machine learning applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/bkvjeq/compressio
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