This paper presents a novel method for a data-driven training of
affine-linear predictors which perform intra prediction in state-ofthe-
art video coding. The main aspect of our training design is the
use of subband decomposition of both the input and the output of the
prediction. Due to this architecture, the same set of predictors can be
shared across different block shapes leading to a very limited memory
requirement. Also, the computational complexity of the resulting
predictors can be limited such that it does not exceed the complexity
of the conventional angular intra prediction. In the training
itself, a loss function modelling the bit-rate of the DCT-transformed
residuals is used. The obtained predictors are incorporated into the
Versatile Video Coding Test Model 3 in addition to the conventional
intra prediction modes. All-Intra bit-rate savings ranging from 0.8%
to 1.4% across different resolutions have been measured in terms of
the Bjoentegaard-Delta bit rate (BD-rate).