This is the second edition of the book. When compared to the first edition [Zbl 0903.94001], the authors have introduced some minor changes. They have reorganized Chapter 2 – “Information and entropy” – putting emphasis on Shannon’s works, e.g. Shannon’s quantification of information. Chapter 3 is also slightly modified. The authors have added a new part on the capacity of \(n\)-any symmetric channels. In turn, in Chapter 4, a new Section on “The information rate of a code” has been appended.
The book covers most of the elementary problems related to both information theory and data compression. The authors give the basic definitions and theorems from both areas – explaining their meaning through practical exercises. The reader can find fundamentals of the theory of probability, information theory – including definitions of information, entropy, channel models and channel capacity bounds, coding theory – with Shannon’s theorem and the Kraft-McMillan inequality, lossless data compression schemes – consisting of Huffman’s, Fano’s and Shannon’s encoding schemes, as well as adaptive (e.g. Knuth’s and Gallager’s methods) and dictionary methods.
The book also gives certain, however very limited, information on transformation methods and image compression algorithms (JPEG).
More advanced pieces of information are attached in the form of appendices. The appendices also include notes on and solutions to some exercises. All of this makes the book a useful compendium of fundamental knowledge on information theory and data compression principles.