Content Adaptive Image Compression
High throughput screening with imaging systems is growing increasingly important in biomedical applications and material sciences too. Extremely large quantities of image data are generated and the images are usually compressed to save memory. There are universal methods, chiefly transformations, suited for rapid (on-the-fly) compression (e.g. cosine transforms in JPEG), and slower (less data loss or higher compression) methods (wavelet transform contained in JPEG2000) used more for archiving. Each of these methods, however, treats every image separately and de facto independently of its content. Taking this as the point of departure, compression can also be executed as a function of the typical content of an image from a larger set of images. A number of artificial neural networks are trained for specific contents of images and their compression. Investing effort in this one-time training is worthwhile whenever a large number of similar images will be saved later, e.g. images (microscopy, CT, NMR) of an identical tissue or organ. This is often the case in biological and medical applications. The advantage over universal methods is the higher compression performance through the a priori information on the dominant image content incorporated in the compression. Comparisons with similar image formats (e.g. JPEG, JPEG2000, PNG) as well as universal compression algorithms (e.g. ZIP) have demonstrated this method’s superiority. The method can be broadly parameterized – from nearly lossless to extremely high compression rates. By selecting typical image contents to train the compressors, the quality of reconstructed images to also be rendered as a function of content, i.e. relevant structures are compressed less and thus with less loss than other regions of images. This feature is independent of the local appearance of independent structures in an image. The integration of this system in commercial image acquisition and/or storage systems is planned for the near future.