Image Compression Using Fractals and Wavelets
Abstract
There are several approaches to image compression. The current most popular methods relies on eliminating high frequency components of the signal by storing only the low frequency Fourier coefficients. Other methods use a 'building block' approach, breaking up images into a small number of canonical pieces and storing only a reference to which piece goes where. Our research has focused on a new scheme based on fractals. Our approach to image compression is to tessellate the image with a tiling which varies with the local image complexity, and to check for self similarity amongst the tiles. Self similarities are coded as systems of affine transformations which can be stored far more compactly than the original images on small platforms. An original objective of our Phase II research project was to develop a hardware implementation of our Fractal based algorithm and to investigate various techniques for encoding. During the course of initial investigations it became apparent that the bulk of our efforts should be directed toward speeding up the software algorithms, especially in the decoding and adapting our fractal encoding methods to color images. This is because the most significant commercial applications of image compression require fast decoding.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 02, 1993
- Accession Number
- ADA266074