Content Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Networks

Abstract

Biomedical High-Throughput Screening (HTS) requires specific properties of image compression. Particularly especially when archiving a huge number of images of one specific experiment the time factor is often rather secondary, and other features like lossless compression and a high compression ratio are much more important. Due to the similarity of all images within one experiment series, a content based compression seems to be especially applicable. Biologically inspired techniques, particularly Artificial Neural Networks (ANN) are an interesting and innovative tool for adaptive intelligent image compression, although a couple of promising non-neural alternatives, such as CALIC or JP EG2000 have become available.