This book provides a basic introduction to both information theory and data compression. Although the two topics are related, this unique treatment allows readers to explore either topic independently. The authors' presentation of information theory is pitched at an elementary level, making the book less daunting than most other texts. The second edition includes a detailed history of information theory that provides a solid background for the quantification of the topic as developed by Claude Shannon. It also covers the information rate of a code and the trade-off between error correction and rate of information transmission, probabilistic finite state source automata, and wavelet methods.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
An effective blend of carefully explained theory and practical applications, this book imparts the basics of both information theory and data compression. Although the two topics are related, this unique treatment allows readers to explore either topic independent of the other.
The authors' treatment of Information Theory, while theoretical and abstract, is pitched at an elementary level, less daunting than most other texts. After presenting the fundamental definitions and results of the theory, they then bring the theory to bear on noisy channels. A number of computational examples and exercises are included.
The Data Compression section acquaints readers with a myriad of lossless compression methods and introduces then to the weirder world of lossy compression and how one can proceed using various transforms. This section allows readers to emerge broadly conversant with and competent in a large range of techniques.
The unique, flexible presentation in Introduction to Information Theory and Data Compression provides the background needed to apply information theory to forming and answering theoretical questions in data compression. However, those curious about data compression, but with no interest in information theory and vice versa can profit equally from the book-as can those with a curiosity about the intriguing connections between the two areas.
This book provides a basic introduction to both information theory and data compression. Although the two topics are related, this unique treatment allows readers to explore either topic independently. The authors' presentation of information theory is pitched at an elementary level, making the book less daunting than most other texts. The second edition includes a detailed history of information theory that provides a solid background for the quantification of the topic as developed by Claude Shannon. It also covers the information rate of a code and the trade-off between error correction and rate of information transmission, probabilistic finite state source automata, and wavelet methods.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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