05-compression-02-entropy coding.pptx - ICSY

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Arithmetic Coding } probability model is separated from en<strong>coding</strong> operation (cont.) - En<strong>coding</strong>: • given subinterval can be uniquely identified by any value of that interval • Use the value with the shortest non-ambiguous binary representation to identify the subinterval } In practice, the subinterval is refined incrementally using the probabilities of the individual events. - If strings of different length are used, extra information is needed to detect the end of a string Prof. Dr. Paul Müller, University of Kaiserslautern 24
Arithmetic Coding } Example I - Four characters a, b, c, d with probabilities p(a)=0.4, p(b)=0.2, p (c)=0.1, p(d)=0.3. Codeword assignment for string "abcd": à codeword (subinterval): [0.21360, 0.21600) Prof. Dr. Paul Müller, University of Kaiserslautern 25
Page 1 and 2: Entropy Coding } different probabil
Page 3 and 4: The Braille Prof. Dr. Paul Müller,
Page 5 and 6: Morse Code Example: Letter statisti
Page 7 and 8: Huffman Coding } Idea: Use short bi
Page 9 and 10: Huffman Coding Symbol Count P2 205
Page 11 and 12: Huffman Coding Symbol Count Huffman
Page 13 and 14: Adaptive Huffman Coding } Problem -
Page 15 and 16: Adaptive Huffman Coding } Example n
Page 17 and 18: Adaptive Huffman Coding } A was inc
Page 19 and 20: Adaptive Huffman Coding } n+4-th Hu
Page 21 and 22: Huffman Coding Evaluation } Limitat
Page 23: Arithmetic Coding } arithmetic codi
Page 27 and 28: Arithmetic Coding } Example I (cont
Page 29 and 30: Arithmetic Coding Example II (cont.
Page 31 and 32: Arithmetic Coding } Remarks - if th
Page 33 and 34: Lempel - Ziv (LZ77) } Algorithm for
Page 35 and 36: Lempel - Ziv (LZ77) } Remarks - the
Page 37 and 38: Lempel - Ziv - Welch (LZ78) } LZ78:

05-compression-02-entropy coding.pptx - ICSY

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