Modern microscopic techniques like high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging may involve collection of thousands of images per experiment. Efficient image-compression techniques are indispensable to manage these vast amounts of data. This goal is frequently achieved using lossy compression algorithms such as JPEG and JPEG2000. However, these algorithms are optimized to preserve visual quality but not necessarily the integrity of the scientific data, which are often analyzed in an automated manner. Here, we propose three observer-independent compression algorithms, designed to preserve information contained in the images. These algorithms were constructed using signal-to-noise ratio (SNR) computed from a single image as a quality measure to establish which image components may be discarded. The compression efficiency was measured as a function of image brightness and SNR. The alterations introduced by compression in biological images were estimated using brightness histograms (earth's mover distance (EMD) algorithm) and textures (Haralick parameters). Furthermore, a microscope test pattern was used to assess the effect of compression on the effective resolution of microscope images.