  Inpainting-based image compression is a promising alternative to classical
transform-based lossy codecs. Typically it stores a carefully selected subset
of all pixel locations and their colour values. In the decoding phase the
missing information is reconstructed by an inpainting process such as
homogeneous diffusion inpainting. Optimising the stored data is the key for
achieving good performance. A few heuristic approaches also advocate
alternative feature types such as derivative data and construct dedicated
inpainting concepts. However, one still lacks a general approach that allows to
optimise and inpaint the data simultaneously w.r.t. a collection of different
feature types, their locations, and their values. Our paper closes this gap. We
introduce a generalised inpainting process that can handle arbitrary features
which can be expressed as linear equality constraints. This includes e.g.
colour values and derivatives of any order. We propose a fully automatic
algorithm that aims at finding the optimal features from a given collection as
well as their locations and their function values within a specified total
feature density. Its performance is demonstrated with a novel set of features
that also includes local averages. Our experiments show that it clearly
outperforms the popular inpainting with optimised colour data with the same
density.
