## 1 Introduction  
DNA microarray technology allows the analysis of the expression of thousands of genes in a single experiment and has become a very important tool in medicine and biology for the study of genetic function, regulation and interaction [1]. Genome-wide monitoring is possible with existing DNA microarrays, which are used in research against cancer [2] and HIV [3], among many other applications. DNA microarrays consist of a solid surface on which thousands of known genetic sequences are bound. Each sequence is placed in one microscopic hole or spot and all spots are arranged conforming to a regular pattern. Two samples, for example from healthy and tumoral tissue, are labeled, respectively, with green and red fluorescent markers called Cy3 and Cy5. Then, equal amounts of the labeled samples are made to react with the genetic sequences on the microarray. If one sample has an expressed sequence corresponding to a sequence placed in the microarray, part of it is hybridized and fixed in the correspondent spot; else, it gets washed away and will not be present in the spot. Once the hybridization and washing have concluded, the microarray is exposed to ultraviolet light so that the emissions from the fluorescent Cy3 and Cy5 dyes can be scanned and registered. Spots whose corresponding sequences are more strongly expressed in the first sample will have more Cy3 dye present and thus will emit more intense green light. The same can be said for the second sample and the red Cy5 dye. Comparing the relative intensity of the green and red channels, it is possible to detect which genes have not been equally expressed in both samples. This can be used to hypothesize about the function of individual genes under many different conditions. The output of a microarray experiment is a pair of monochrome images, one for the green channel and another for the red channel. An example microarray image can be seen in Figure 1. Due to the microscopic size of the spots, images have a high spatial resolution and are of large dimensions. Images from \(1000\times 1000\) onwards are described in the literature, but sizes over \(4000\times 13000\) are common nowadays. Since gene expression can vary in a very wide range, image pixel intensities have a depth of \(16\) bits per pixel (bpp). Figure 1: \(940\times 910\) portion of red channel image y744n43_ch2.tif from the Yeast dataset. Gamma levels have been adjusted in the figure for better viewing. Microarray images are computer analyzed to obtain genetic information. However, it is not desirable to keep only this genetic information and discard the microarray images. Analysis techniques are not fully mature or universally accepted and are subject to change. Furthermore, repeating an experiment is expensive and not always possible. Depending on the microarray size and the scanner spatial resolution, raw data for a single channel can require from a few to hundreds of Megabytes. With the increasing interest in DNA microarrays, and since many experiments are conducted under several different conditions, great amounts of data are generated in laboratories around the world. Because of the need of keeping and sharing microarray images, a need for efficient storage and transmission arises, and so compression emerges as a natural approach. The role of data compression in computational biology and the state of the art has been addressed by several authors [4; 5]. Both lossy and lossless techniques have been proposed in the literature. Lossy approaches exhibit good compression performance on microarray images, but there is an open debate on whether information loss is acceptable or not, since it can alter genetic information extraction results. On the other hand, purely lossless methods guarantee immutable extraction results but offer poorer compression performance. This is partly due to the considerable amount of noise and the abundance of high frequencies present in this type of image. This paper is structured as follows.