Introduction to Information Theory and Data Compression (2nd Ed.) Applied Mathematics Series
Auteurs : Johnson Jr., Harris Greg A., Hankerson D.C.
An effective blend of carefully explained theory and practical applications, this text imparts the fundamentals of both information theory and data compression. Although the two topics are related, this unique text allows either topic to be presented independently, and it was specifically designed so that the data compression section requires no prior knowledge of information theory.
The treatment of information theory, while theoretical and abstract, is quite elementary, making this text less daunting than many others. After presenting the fundamental definitions and results of the theory, the authors then apply the theory to memoryless, discrete channels with zeroth-order, one-state sources.
The chapters on data compression acquaint students with a myriad of lossless compression methods and then introduce two lossy compression methods. Students emerge from this study competent in a wide range of techniques. The authors' presentation is highly practical but includes some important proofs, either in the text or in the exercises, so instructors can, if they choose, place more emphasis on the mathematics.
Introduction to Information Theory and Data Compression, Second Edition is ideally suited for an upper-level or graduate course for students in mathematics, engineering, and computer science.
Features:
Date de parution : 09-2019
15.6x23.4 cm
Date de parution : 02-2003
Ouvrage de 416 p.
15.2x22.9 cm
Thèmes d’Introduction to Information Theory and Data Compression :
Mots-clés :
Source Alphabet; Source Letter; Huffman’s Algorithm; Binary Expansion; Binary Words; Binary Symmetric Channel; Relative Frequency; Finite Probability Space; Arithmetic Coding; Code Word Length; Encoding Scheme; Input Alphabet; Huffman Encoding; Compression Ratio; Source Word; Input Frequencies; Code Word; Huffman Tree; Output Alphabet; Prefix Condition; Interval Encoding; Sine Transforms; Uniquely Decodable; Cosine Transform; Leaf Nodes