Fractal and Wavelet Image Compression Techniques (SPIE Tutorial Texts in Optical Engineering Vol. TT40)

Interest in image compression for Internet and other multimedia applications has spurred research into compression techniques that will increase storage capabilities and transmission speed. This tutorial provides a practical guide to fractal and wavelet approaches--two techniques with exciting potential. It is intended for scientists, engineers, researchers, and students. It provides both introductory information and implementation details. Three Windows-compatible software systems are included so that readers can explore the new technologies in depth. Complete C/C++ source code is provided, enabling readers to go beyond the accompanying software. The mathematical presentation is accessible to advanced undergraduate or beginning graduate students in technical fields.

Contents

- Preface
- Introduction
- Iterated Function Systems
- Fractal Encoding of Grayscale Images
- Speeding Up Fractal Encoding
- Simple Wavelets
- Daubechies Wavelets
- Wavelet Image Compression Techniques
- Comparison of Fractal and Wavelet Image Compression
- References
- Appendix A: Using the Accompanying Software
- Appendix B: Utility Windows Library (UWL)
- Appendix C: Organization of the Accompanying Software Source Code

Stephen Welstead received the Ph.D. degree in applied mathematics from Purdue University. He is the author of ''Neural Network and Fuzzy Logic Applications in C/C++'' and numerous papers and presentations on neural networks, fractal image encoding, chaos and dynamical systems, and signal processing, along with several articles on Windows programming techniques. He has taught short courses on fractal and wavelet image compression, neural networks, and fuzzy logic. He also has been adjunct associate professor of mathematics at the University of Alabama at Huntsvelle, where he developed and taught a graduate-level course on fractal geometry. He is a member of SPIE--The International Society for Optical Engineering, The Institute of Electrical and Electronics Engineers (IEEE), and the Society of Industrial and Applied Mathematics (SIAM).