448-1 | Data transmission: compression and coding | Computer Science | S9 | ||||||
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Lessons : 25 h | TD : 0 h | TP : 0 h | Project : 0 h | Total : 25 h | |||||
Co-ordinator : Sébastien Fourey |
Prerequisite | |
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Discrete mathematics, linear algebra, combinatorics, probabilities, algorithmics, software programming |
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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. |
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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. |
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Practical work (TD or TP) | |
No labs |
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Acquired skills | |
Knowledge about error correcting codes and classical data compression methods. |
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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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