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Content Adaptive Compression of Images Using Neural Maps Content Adaptive Compression of Images Using Neural Maps

High-Throughput Screening (HTS) in biomedical environment is often based on image acquisition and processing. In many of these cases the images are characterised by two properties – (i) they are in great quantities and high resolution, and (ii) they contain limited and similar matter. The first property leads to an enormous demand of storage capacity making any image compression appropriate, while the second one paves the way for content adaptive image compression. However, this requires an easily adaptable and reliable image content selection method. By means of Neural Maps this paper demonstrates how unsupervised clustering can handle this.