Lectures

1. Intro: Taxonomy of compression methods, data models (probabilistic, Markov).
2. Intro: Required notions from information theory and coding (entropy, optimal prefix code)
3. Intro: Basic techniques (RLE, MTF) and integer coding (Elias codes).
4. Statistical methods: Shannon-Fano and Huffman coding, principles and implementation.
5. Statistical methods: Arithmetic and QM coding, principles and implementation.
6. Context-based methods: PPM and PAQ (context mixing) methods, principles and implementation.
7. Context-based methods: Block sorting (Burrows-Wheeler transform, BWT), principles and implementation.
8. Dictionary methods: LZ77 methods family and Deflate variation, principles and implementation.
9. Dictionary methods: LZ78 methods family and LZW variation, principles and implementation.
10. Other lossless methods: Grammar-based, statistical and other selected methods.

Literature

• Sayood K.: Introduction to Data Compression, Fifth Edition. Morgan Kaufmann, 2018. ISBN 978-0128094747.
• Salomon D., Motta G.: Handbook of Data Compression, 5th edition. Springer, 2010. ISBN 978-1848829022.
• McAnlis C., Haecky A.: Understanding Compression: Data Compression for Modern Developers. O'Reilly, 2016. ISBN 878-1491961537
• Mengyi Pu I.: Fundamental Data Compression. Butterworth-Heinemann, 2006. ISBN 978-0750663106.
• Salomon D.: Data Compression: The complete Reference, 4th edition. Springer, 2006. ISBN 978-1846286025.
• Hankerson D. C., Harris G. A., Johnson P. D.: Introduction to Information Theory and Data Compression, Second Edition (Applied Mathematics). Chapman and Hall/CRC, 2003. ISBN 978-1584883135.
• Sayood K.: Lossless compression handbook. Academic Press, 2003. ISBN 0126208611.