Adapting Learned Image Codecs to Screen Content via Adjustable
  Transformations
H. Burak Dogaroglu A. Burakhan Koyuncu Atanas Boev Elena Alshina Eckehard Steinbach
As learned image codecs (LICs) become more prevalent, their low coding
efficiency for out-of-distribution data becomes a bottleneck for some
applications. To improve the performance of LICs for screen content (SC) images
without breaking backwards compatibility, we propose to introduce parameterized
and invertible linear transformations into the coding pipeline without changing
the underlying baseline codec's operation flow. We design two neural networks
to act as prefilters and postfilters in our setup to increase the coding
efficiency and help with the recovery from coding artifacts. Our end-to-end
trained solution achieves up to 10% bitrate savings on SC compression compared
to the baseline LICs while introducing only 1% extra parameters.