Iterative training of neural networks for intra prediction

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, that is −1.8% above the state-of-the-art. By moving them into H.266 (VTM-5.0), the mean dB-rate reduction reaches −1.9%.

256×256 portion of the luminance channel of the first frame of PartyScene

Reconstruction at QP=32 via VTM-5.0 with our neural network-based mode replacing MIP