The properties of prints are not fully determined by the materials they are composed of and the method that was used to compose them. These merely set limits to what a print's properties, such as its colors, sharpness, smoothness, color inconstancy and level of ink use, will be and it is the role of a printing system's imaging pipeline to select a particular combination. Conventionally such choices are implicit in how resources for a pipeline are built and can be improved with experience and trial an error. Performance can be improved though by optimizing for specific attributes, as was previously shown for color consistency, ink use and grain among others. A key constraint that remains here is that optimization is performed on the basis of sampling and search strategies, which have inherent limitations. This paper presents a direct, analytical approach to optimization that hinges on the insight that it can be performed in a convex space even when the properties involved in the optimization do not relate to each other in a convex way. The result both improves performance versus previous methods and does so in considerably less time.
Peter Morovic, Hector Gómez, Ján Morovic, Pere Gasparin, Tanausú Ramírez, Xavier Fariña, Sergio Etchebehere, "Revisiting print-attribute optimization: a direct pattern generation approach" in Proc. IS&T 28th Color and Imaging Conf., 2020, pp 299 - 306, https://doi.org/10.2352/issn.2169-2629.2020.28.48