  In recent years, it has been found that screen content images (SCI) can be
effectively compressed based on appropriate probability modelling and suitable
entropy coding methods such as arithmetic coding. The key objective is
determining the best probability distribution for each pixel position. This
strategy works particularly well for images with synthetic (textual) content.
However, usually screen content images not only consist of synthetic but also
pictorial (natural) regions. These images require diverse models of probability
distributions to be optimally compressed. One way to achieve this goal is to
separate synthetic and natural regions. This paper proposes a segmentation
method that identifies natural regions enabling better adaptive treatment. It
supplements a compression method known as Soft Context Formation (SCF) and
operates as a pre-processing step. If at least one natural segment is found
within the SCI, it is split into two sub images (natural and synthetic parts),
and the process of modelling and coding is performed separately for both. For
SCIs with natural regions, the proposed method achieves a bit-rate reduction of
up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.
