Abstract
In this chapter, data compression as it relates to multimedia information is studied from the point of view of lossless algorithms, where the input data is essentially exactly recoverable from the compressed data Lossy algorithms, for which this is not the case, are presented in Chapter 8. Here we introduce the fundamentals of information theory and algorithms whose goal is a savings in bitrate given the entropy, especially Huffman Coding and its adaptive version. We then study Dictionary-based Coding (as in Winzip) and go on to a detailed discussion of Arithmetic Coding. Finally, Lossless Image Compression is examined specifically.
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Notes
- 1.
Since we have chosen 2 as the base for logarithms in the above definition, the unit of information is bit—naturally also most appropriate for the binary code representation used in digital computers. If the log base is 10, the unit is the hartley; if the base is \(e\), the unit is the nat.
- 2.
An information source that is independently distributed, meaning that the value of the current symbol does not depend on the values of the previously appeared symbols.
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Li, ZN., Drew, M.S., Liu, J. (2014). Lossless Compression Algorithms. In: Fundamentals of Multimedia. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-05290-8_7
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DOI: https://doi.org/10.1007/978-3-319-05290-8_7
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