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Data transmission: compression and coding

448-1 Data transmission: compression and coding Computer Science S9
Lessons : 25 h TD : 0 h TP : 0 h Project : 0 h Total : 25 h
Co-ordinator : Sébastien Fourey
Prerequisite
Discrete mathematics, linear algebra, combinatorics, probabilities, algorithmics, software programming
Course Objectives
Data compression (13 hours - S. Fourey) : Generic principles of text, still image and video compression, and associated standard methods.

Error correcting codes (12 hours - M. Barbier) : This course aims at providing the minimal knowledge to understand the encoding systems used to protect any information against errors it is subject to during transmission.
Syllabus
1. Data compression : Basics of information theory. Classical compression methods : RLE, Huffman, LZ*, arithmetic coding. Vector quantization. Lossless and lossy image compression : PNG, JPEG. Video compression.

2. Error correcting codes : The problem behind channel coding, Shannon theorems, properties of linear codes, definition of the Hamming codes and their decoding, definition of the Reed-Solomon codes and their decoding.
Practical work (TD or TP)
No labs
Acquired skills
Knowledge about error correcting codes and classical data compression methods.
Bibliography
1. The Theory of Error-Correcting Codes, F. J. MacWilliams and N. J. A. Sloane, North-Holland, Amsterdam (1977).
2. Codes correcteurs, Théorie et applications, A. Poli, L. Huguet, Masson, Paris (1989).
3. A Course in Error-Correcting Codes, Jørn Justesen and Tom Høholdt (2004).
4. Information Theory, Inference, and Learning Algorithms, D. MacKay (2003).
5. Modern Coding Theory, R. Urbanke, T. Richardson (2005).
6. Compression d'images : Algorithmes et standards, Éric Incerti, Vuibert (2003).

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