  Machine learning techniques for more efficient video compression and video
enhancement have been developed thanks to breakthroughs in deep learning. The
new techniques, considered as an advanced form of Artificial Intelligence (AI),
bring previously unforeseen capabilities. However, they typically come in the
form of resource-hungry black-boxes (overly complex with little transparency
regarding the inner workings). Their application can therefore be unpredictable
and generally unreliable for large-scale use (e.g. in live broadcast). The aim
of this work is to understand and optimise learned models in video processing
applications so systems that incorporate them can be used in a more trustworthy
manner. In this context, the presented work introduces principles for
simplification of learned models targeting improved transparency in
implementing machine learning for video production and distribution
applications. These principles are demonstrated on video compression examples,
showing how bitrate savings and reduced complexity can be achieved by
simplifying relevant deep learning models.
