Fundamental Data Compression - 1st Edition - ISBN: 9780750663106, 9780080530260

Fundamental Data Compression

1st Edition

Authors: Ida Pu
eBook ISBN: 9780080530260
Paperback ISBN: 9780750663106
Imprint: Butterworth-Heinemann
Published Date: 3rd November 2005
Page Count: 256
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Description

Fundamental Data Compression provides all the information students need to be able to use this essential technology in their future careers. A huge, active research field, and a part of many people's everyday lives, compression technology is an essential part of today's Computer Science and Electronic Engineering courses.

With the help of this book, students can gain a thorough understanding of the underlying theory and algorithms, as well as specific techniques used in a range of scenarios, including the application of compression techniques to text, still images, video and audio. Practical exercises, projects and exam questions reinforce learning, along with suggestions for further reading.

Key Features

  • Dedicated data compression textbook for use on undergraduate courses
  • Provides essential knowledge for today's web/multimedia applications
  • Accessible, well structured text backed up by extensive exercises and sample exam questions

Readership

Advanced undergraduate students studying data compression as part of a computer science degree, as well as web design, information systems and mathematics students. Postgraduate students and professionals needing an accessible introductory data compression text.

Table of Contents

Dedication

List of Figures

List of Algorithms

Preface

Acknowledgements

Chapter 1: Introduction

1.1 Data compression problems

1.2 Lossless and lossy compression

1.3 Deriving algorithmic solutions

1.4 Measure of compression quality

1.5 Limits on lossless compression

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 2: Coding symbolic data

2.1 Information, data and codes

2.2 Symbolic data

2.3 Variable length codes

Chapter 3: Run-length algorithms

3.1 Run-length

3.2 Hardware data compression (HDC)

3.3 Algorithm Design

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 4: Huffman coding

4.1 Static Huffman coding

4.2 Shannon-Fano approach

4.3 Optimal Huffman codes

4.4 Implementation efficiency

4.5 Extended Huffman coding

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 5: Adaptive Huffman coding

5.1 Adaptive approach

5.2 Compressor

5.3 Decompressor

5.4 Disadvantages of Huffman algorithms

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 6: Arithmetic coding

6.1 Probabilities and subintervals

6.2 Model and coders

6.3 Simple case

6.4 General case

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 7: Dictionary-based compression

7.1 Patterns in a string

7.2 LZW coding

7.3 LZ77 family

7.4 LZ78 family

7.5 Applications

7.6 Comparison

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 8: Prediction and transforms

8.1 Predictive approach

8.2 Move to Front coding

8.3 Burrows-Wheeler Transform (BWT)

8.4 Transform approach

8.5 Discrete Cosine Transform (DCT)

8.6 Subband coding

8.7 Wavelet transforms

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 9: Audio compression

9.1 Modelling sound

9.2 Sampling

9.3 Quantisation

9.3.1 Scalar quantisation

9.4 Compression performance

9.5 Speech compression

9.6 Music compression

Summary

Learning outcomes

Exercises

Assessment

Chapter 10: Image compression

10.1 Image data

10.2 Bitmap images

10.3 Vector graphics

10.4 Bitmap and vector graphics

Rasterising

Vectorisation

10.5 Colour

10.6 Classifying images by colour

10.7 Classifying images by appearance

10.8 Image compression

Summary

Learning outcomes

Exercises

Laboratory

Assessment

Chapter 11: Video compression

11.1 Analogue video

11.2 Digital video

11.3 Moving pictures

11.4 MPEG

11.5 Basic principles

11.6 Temporal compression algorithms

11.7 Group of pictures

11.8 Motion estimation

11.9 Work in different video formats

Summary

Learning outcomes

Exercises

Assessment

Appendix A: Brief history

Appendix B: Matrices

Appendix C: Fourier series and harmonic analysis

Appendix D: Pseudocode notation

Appendix E: Notation

Index

Details

No. of pages:
256
Language:
English
Copyright:
© Butterworth-Heinemann 2005
Published:
Imprint:
Butterworth-Heinemann
eBook ISBN:
9780080530260
Paperback ISBN:
9780750663106

About the Author

Ida Pu

Affiliations and Expertise

Department of Computing, Goldsmiths College, University of London, UK