Lossless compression of color-mapped images

Abstract

In a color-mapped (pseudo-color) image, pixel values repre- sent indices that point to color values in a look-up table. Well-known linear predictive schemes, such as JPEG and CALIC, perform poorly when used with pseudo-color images, while universal compressors, such as Gzip, Pkzip and Compress, yield better compression gain. Recently, Burrows and Wheeler introduced the Block Sorting Lossless Data Com- pression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based meth- ods, such as PPM, but at execution speeds closer to Ziv-Lempel tech- niques. The BWA algorithm is mainly composed of a block-sorting trans- formation which is known as Burrows-Wheeler Transformation (BWT), followed by Move-To-Front (MTF) coding. We introduce a new block transformation, Linear Order Transformation (LOT). We delineate its re- lationship to Burrows-Wheeler Transformation and show that LOT is faster than BWT transformation. We then show that when MTF coder is employed after the LOT, the compression gain obtained is better than the well-known compression techniques, such as GIF, JPEG, CALIC, Gzip, LZW (Unix Compress) and the BWA for pseudo-color images.