Nathanael J. Brittain, September 2003--April 2005, "Two Dimensional Dictionary Based Image Compression", degree conferred on Spring 2005 convocation, Computer Science Department, University of Western Ontario, Canada.
M.Sc. Thesis Abstract
Dictionary-based compression methods are a popular form of data file compression. LZ77, LZ78 and their variants are likely the most famous of these methods. These methods are implemented to reduce the one-dimensional correlation in data, since they are designed to compress text. Therefore, they do not take advantage of the fact that, in images, adjacent pixels are correlated in two dimensions. Previous attempts have been made to linearize images in order to make them suitable for dictionary-based compression, but results show that no single linearization is best for all images. Other attempts have been made to adapt dictionary-based compression schemes to consider data in two dimensions, but only for binary images.
In this thesis, a two-dimensional dictionary-based lossless image compression scheme for grayscale images is introduced. The proposed scheme reduces correlation in image data by finding two-dimensional blocks of pixels that are repeated throughout the data and replacing them with short codewords. Test results show that the compression performance of the proposed scheme outperforms and surpasses any other existing dictionary-based compression scheme. The results also show that it is comparable in compression performance to JPEG-LS and JPEG-2000 (when it operates in its lossless mode), where JPEG-2000 and JPEG-LS are the current image compression standards.