Fundamental Data Compression
- 1st Edition - December 28, 2005
- Author: Ida Mengyi Pu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 7 5 0 6 - 6 3 1 0 - 6
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 5 3 0 2 6 - 0
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… Read more
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.
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.
* 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
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.
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
- Edition: 1
- Published: December 28, 2005
- Language: English
IP
Ida Mengyi Pu
Affiliations and expertise
Department of Computing, Goldsmiths College, University of London, UKRead Fundamental Data Compression on ScienceDirect