  The interactions between different tools added successively to a block-based
video codec are critical to its rate-distortion efficiency. In particular, when
deep neural network-based intra prediction modes are inserted into a
block-based video codec, as the neural network-based prediction function cannot
be easily characterized, the adaptation of the transform selection process to
the new modes can hardly be performed manually. That is why this paper presents
a combined neural network-based intra prediction and transform selection for a
block-based video codec. When putting a single neural network-based intra
prediction mode and the learned prediction of the selected LFNST pair index
into VTM-8.0, -3.71%, -3.17%, and -3.37% of mean BD-rate reduction in all-intra
is obtained.
