  With the increasing demand for video content at higher resolutions, it is
evermore critical to find ways to limit the complexity of video encoding tasks
in order to reduce costs, power consumption and environmental impact of video
services. In the last few years, algorithms based on Neural Networks (NN) have
been shown to benefit many conventional video coding modules. But while such
techniques can considerably improve the compression efficiency, they usually
are very computationally intensive. It is highly beneficial to simplify models
learnt by NN so that meaningful insights can be exploited with the goal of
deriving less complex solutions. This paper presents two ways to derive
simplified intra-prediction from learnt models, and shows that these
streamlined techniques can lead to efficient compression solutions.
