Data and especially image compression is becoming increasingly important for efficient resource utilization. Many digital image file formats therefore include universally usable compression methods. They treat every image separately and do not profit from a larger image data set's similar image contents, which are present in numerous biomedical applications. This situation provided the impetus to develop and implement a technical system that incorporates a priori information on typical image contents in image compression on the basis of artificial neural networks and thus increases compression performance for larger image data sets with frequently recurring image contents.
