In a color-mapped (pseudo-color) image, pixel values represent 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 Compression Algorithm (BWA). The BWA algorithm received considerable attention. It achieves compression rates as good as context-based methods, such as PPM, but at execution speeds closer to Ziv-Lempel techniques. The BWA algorithm is mainly composed of a block-sorting transformation 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 relationship 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.