一种改进的分形图像压缩算法

摘要

Image compression is an extremely important part of modern computing．By having the ability to compress images to a fraction of their original size,valuable(and expensive)disk space can be saved．In addition，transportation of images from one computer to another becomes easier and less time consuming(which is why compression has played such an important role in the development of the internet)．Compression of digital images has been a topic of research for many years and a number of image compression standards have been created for different applications．While today more than ever before new technologies provide high speed digital communications and large memories，image compression is still of major importance，because along with the advances in technologies there is increasing demand for image communications，as well as demand for higher quality image printing and display．So far many image compression algorithms have been created and developed，most popular of them are based on Wavelet，JPEG，VQ and Fractal approaches．Each of above mentioned techniques has own advantages and disadvantages，we will try to show it in this thesis． This thesis investigates the whole area of image compression especially we will focus on Fractal image compression method．Fractal image compression gives some desirable properties like resolution independence，fast decoding,and very competitive ratedistortion curves．But still suffers from a(sometimes very)high encoding time,depending on the approach being used．This thesis presents a method to reduce the encoding time of this technique by using Discrete Cosine Transform(DCT)．Experimental results on standard images show that the proposed method yields superior performance over conventional fractal encoding．

目录

英文文摘

论文说明：List of Figures、List of Tables、List of Acronyms

声明

Acknowledgements

1 INTRODUCTION

1.1 What is aDigital Image?

1.2 What is Image Compression?

1.3 Why do we need to compress?

1.3.1 Advantages of Compression

1.3.2 Disadvantages of Compression

1.4 Organization of the Thesis

2 BACKGROUND

2.1 Color Spaces

2.1.1 RGB Space

2.1.2 YUV Space

2.1.3 YIQ Space

2.1.4 YCrCb Space

2.1.5 Comparison of Color Spaces

2.2 Image compression algorithms background

2.2.1 Image classes

2.2.2 Class of applications

2.2.3 Compression algorithm requirements of applications

2.2.4 Criterion of algorithms comparison

2.3 Image Compression Techniques

2.3.1 Entropy coding

2.4 Lossless compression

2.4.1 Run Length Coding (RLE)

2.4.2 LZW Algorithm

2.4.3 Huffman coding

2.4.4 Arithmetic coding

2.4.5 JBIG Algorithm

2.4.6 Lossless JPEG

2.5 Lossy compression

2.5.1 JPEG

2.5.2 Fractal Compression

2.5.3 Wavelet Compression

2.5.4 VQ Compression

2.5.5 Conclusion

2.6 Summary

3 FRACTAL THEORY AND FRACTAL IMAGE COMPRESSION

3.1 What are Fractals?

3.2 Concepts of Fractals

3.3 Mathematical Foundation

3.3.1 Fractal Encoding

3.3.2 Fractal Decoding

3.3.3 Iterations

3.4 Fractal Image Compression

3.4.1 Principle of Fractal Coding

3.4.2 Encoding Images

4 DISCRETE COSINE TRANSFROM

4.1 Introduction

4.2 Formal definition

4.2.1 The One-Dimensional DCT

4.2.2 The Two-Dimensional DCT

4.3 Properties of DCT

4.3.1 Decorrelation

4.3.2 Energy Compaction

4.3.3 Separability

4.3.4 Symmetry

4.3.5 Orthogonality

4.4 DCT versus DFT/KLT

4.5 Summary

5 PROBLEM DEFINITION AND PROPOSED METHOD

5.1 Overview

5.1.1 Previous works

5.2 Description of the algorithm

5.2.1 The DCT

5.2.2 The DCT properties

5.2.3 Fast calculation of DCT

5.2.4 Application of DCT

5.2.5The algorithm construction

5.3 Results

5.3.1 Image quality and compression ratio

5.3.2 Time of compression

5.3.3 Distribution of blocks

6 CONCLUSIONS AND FUTURE WORK

BIBLIOGRAPHY