The term Computational visualistics is used for addressing the whole range of investigating pictures scientifically "in" the computer. Overview Images take a rather prominent place in contemporary life in the western societies. Together with language, they have been connected to human culture from the very beginning. For about one century – after several millennia of written word's dominance – their part is increasing again remarkably. Steps toward a general science of images, which we may call 'general visualistics' in analogy to general linguistics, have only been taken recently. So far, a unique scientific basis for circumscribing and describing the heterogeneous phenomenon "image" in an interpersonally verifiable manner hasstill been missing while distinct aspects falling in the domain of visualistics have predominantly been dealt with in several other disciplines, among them in particular philosophy, psychology, and art history. Last (though not least), important contributions to certain aspects of a new science of images have come from computer science. In computer science, too, considering pictures evolved originally along several more or less independent questions, which lead to proper sub-disciplines: computer graphics is certainly the most "visible" among them. Only just recently, the effort has been increased to finally form a unique and partially autonomous branch of computer science dedicated to images in general. In analogy to computational linguistics, the artificial expression computational visualistics is used for addressing the whole range of investigating scientifically pictures "in" the computer. Areas covered For a science of images within computer science, the abstract data type »image« (or perhaps several such types) stands in the center of interest together with the potential implementations (cf. Schirra 2005). There are three main groups of algorithms for that data type to be considered in computational visualistics: Algorithms from »image« to »image« In the field called image processing, the focus of attention is formed by the operations that take (at least) one picture (and potentially several secondary parameters that are not images) and relate it to another picture. With these operations, we can define algorithms for improving the quality of images (e.g., contrast reinforcement), and procedures for extracting certain parts of an image (e.g., edge finding) or for stamping out pictorial patterns following a particular Gestalt criterion (e.g., blue screen technique). Compression algorithms for the efficient storing or transmitting of pictorial data also belong into this field. Algorithms from »image« to "not-image" Two disciplines share the operations transforming images into non-pictorial