  This paper presents a learning-based method to improve bi-prediction in video
coding. In conventional video coding solutions, the motion compensation of
blocks from already decoded reference pictures stands out as the principal tool
used to predict the current frame. Especially, the bi-prediction, in which a
block is obtained by averaging two different motion-compensated prediction
blocks, significantly improves the final temporal prediction accuracy. In this
context, we introduce a simple neural network that further improves the
blending operation. A complexity balance, both in terms of network size and
encoder mode selection, is carried out. Extensive tests on top of the recently
standardized VVC codec are performed and show a BD-rate improvement of -1.4% in
random access configuration for a network size of fewer than 10k parameters. We
also propose a simple CPU-based implementation and direct network quantization
to assess the complexity/gains tradeoff in a conventional codec framework.
