Abstract
Modern microscopic techniques, like High Content, High Throughput Screening (HCS), may involve collection of thousands of images per experiment. Efficient image compression techniques are indispensable to manage these vast amounts of data. Such compression may be obtained with lossy compression algorithms such as JPEG and JPEG2000. However, these algorithms are optimised to preserve visual quality but not necessarily the integrity of the scientific data. 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. Signal to noise ratio (SNR) was used in this study to construct three lossy compression techniques, which preserve information contained in the images. The compression efficiency was measured as a function of image brightness (and SNR). Furthermore, the alterations introduced by compression were estimated using brightness histograms (earth’s mover distance algorithm) and textures (Haralick parameters).
Published Version
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