Neural-network-based optimal mode estimation for adaptive affine motion compensation
Takahiro Kitamura, Toshiyuki Yoshida · 2021
Affine motion compensation (AMC) techniques are attracting much attention as one of promising tools in video coding. As an introduction of AMC increases the number of MC modes, an efficient mode selection technique is necessary to maximize the potential of AMC, which actually requires quite high computation cost. This paper thus tries to estimate the optimal mode in an AMC scenario using a neural network (NN). The experimental result indicates that our NN gives an efficient model selection technique in term the rate-distortion-based criteria.