  Finding optimal data for inpainting is a key problem in the context of
partial differential equation based image compression. The data that yields the
most accurate reconstruction is real-valued. Thus, quantisation models are
mandatory to allow an efficient encoding. These can also be understood as
challenging data clustering problems. Although clustering approaches are well
suited for this kind of compression codecs, very few works actually consider
them. Each pixel has a global impact on the reconstruction and optimal data
locations are strongly correlated with their corresponding colour values. These
facts make it hard to predict which feature works best.
  In this paper we discuss quantisation strategies based on popular methods
such as k-means. We are lead to the central question which kind of feature
vectors are best suited for image compression. To this end we consider choices
such as the pixel values, the histogram or the colour map.
  Our findings show that the number of colours can be reduced significantly
without impacting the reconstruction quality. Surprisingly, these benefits do
not directly translate to a good image compression performance. The gains in
the compression ratio are lost due to increased storage costs. This suggests
that it is integral to evaluate the clustering on both, the reconstruction
error and the final file size.
