Reiner Eschbach

Simple spatial processing for color mappings

In image processing, color is generally treated as an autonomous entity that is separate from spatial dimensions. Consequently, color processing and color mapping are predominantly done as point operations, linking one color to another, without considering spatially neighboring colors. It is well known that this point-wise approach does not capture the spatial dependencies of color that we humans experience in everyday life. More precise mathematical models of color vision that take into account spatial dependencies are generally computationally expensive. This leads to a continuing predominance of point-wise color processing. We introduce some simple spatially dependent processing techniques and demonstrate the potential advantages of even such simple schemes. The underlying assumption is that simple spatial operations allow the hiding of artifacts and errors in a way that is less objectionable to a human observer.

Comparing image preference in controlled and uncontrolled viewing conditions

We examine the relationship between controlled and uncontrolled visual preference tests. We compare the results for the preference of printed images in various viewing environments. The data are examined with regard to different numbers of observer subsets and we can derive an experimental guideline for an equivalence of the controlled and uncontrolled preference experiments based on the certainty of the expected result.