  This paper describes a set of neural network architectures, called Prediction
Neural Networks Set (PNNS), based on both fully-connected and convolutional
neural networks, for intra image prediction. The choice of neural network for
predicting a given image block depends on the block size, hence does not need
to be signalled to the decoder. It is shown that, while fully-connected neural
networks give good performance for small block sizes, convolutional neural
networks provide better predictions in large blocks with complex textures.
Thanks to the use of masks of random sizes during training, the neural networks
of PNNS well adapt to the available context that may vary, depending on the
position of the image block to be predicted. When integrating PNNS into a H.265
codec, PSNR-rate performance gains going from 1.46% to 5.20% are obtained.
These gains are on average 0.99% larger than those of prior neural network
based methods. Unlike the H.265 intra prediction modes, which are each
specialized in predicting a specific texture, the proposed PNNS can model a
large set of complex textures.
