  This paper presents an iterative training of neural networks for intra
prediction in a block-based image and video codec. First, the neural networks
are trained on blocks arising from the codec partitioning of images, each
paired with its context. Then, iteratively, blocks are collected from the
partitioning of images via the codec including the neural networks trained at
the previous iteration, each paired with its context, and the neural networks
are retrained on the new pairs. Thanks to this training, the neural networks
can learn intra prediction functions that both stand out from those already in
the initial codec and boost the codec in terms of rate-distortion. Moreover,
the iterative process allows the design of training data cleansings essential
for the neural network training. When the iteratively trained neural networks
are put into H.265 (HM-16.15), -4.2% of mean dB-rate reduction is obtained. By
moving them into H.266 (VTM-5.0), the mean dB-rate reduction reaches -1.9%.
