ITERATIVE TRAINING OF NEURAL NETWORKS FOR INTRA PREDICTION

摘要

An iterative training of neural networks for video coding and decoding using intra prediction is provided that finds a tradeoff between an extreme genericity and an extreme specialization to a codec for the trained neural networks. At the first iteration, the set of neural networks is trained following a partitioning approach. Then, for several iterations, the set of neural networks is inserted into the codec, and pairs of a block and its context are extracted from the partitioning of images via the codec with a single additional neural network-based mode then, the neural networks are retrained on these pairs. This way, from the second iteration, the neural networks learn an intra prediction diverging from that in the codec while still being valuable for the codec in terms of rate-distortion performance.