Ebook A Concise Introduction to Data Compression (Undergraduate Topics in Computer Science), by David Salomon
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A Concise Introduction to Data Compression (Undergraduate Topics in Computer Science), by David Salomon
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Compression is an option naturally selected when faced with problems of high costs or restricted space. This book offers readers a succinct and reader-friendly foundation to the most important topics in the field of data compression.
Divided into two parts, Part I presents the basic approaches to data compression and describes a few popular techniques and methods that are commonly used to compress data. The reader discovers essential concepts, such as variable-length and prefix codes, statistical distribution and run-length codes. Part II concentrates on advanced techniques, such as arithmetic coding, orthogonal transforms, subband transforms and Burrows-Wheeler transform.
Complete and clear, this book will be the perfect reference for advanced undergraduates in computer science and requires a minimum of mathematics. An author-maintained website provides readers with errata and auxiliary material.
- Sales Rank: #1712086 in eBooks
- Published on: 2007-12-18
- Released on: 2007-12-18
- Format: Kindle eBook
Most helpful customer reviews
4 of 9 people found the following review helpful.
Very hard to follow text book
By Clifford W. Lazar
The author doesn't see fit to show the inputs and the outputs of each of the processes.
A terrible example is LZW. We know there is a processed buffer and a look-ahead buffer. We don't know the step-by-step process.
2 of 3 people found the following review helpful.
concise, precise and easy. What's not to like?
By Book Lover
I really really loved this book.
I'm a self-taught programmer, and this book is one of the easiest introductions to the subject. The language is simple, the examples are clear and unhurried, and the important topics are covered. In fact, I understood basic concepts of information theory here for the first time. For example, while everybody else says "the information content of a random variable is \sigma(P_i log(P_i))", this books takes a couple of pages to explain the intuition behind this formula.
For the fact that this is a concise guide, it packs quite a bit of information, from LZW, Huffman, zip, arithmetic coding, wavelets, jpeg and audio compression (just to pick a few). The best way to read this book is to have a calculator handy, and to hand-do the examples from the description of the algorithm _before_ you read the solution.
This is a keeper.
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