Neural-network-based optimal mode estimation for adaptive affine motion compensation

Takahiro Kitamura; Toshiyuki Yoshida

doi:10.1117/12.2591016

13 March 2021 Neural-network-based optimal mode estimation for adaptive affine motion compensation

Takahiro Kitamura, Toshiyuki Yoshida

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117662A (2021) https://doi.org/10.1117/12.2591016
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

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.

Citation Download Citation

Takahiro Kitamura and Toshiyuki Yoshida "Neural-network-based optimal mode estimation for adaptive affine motion compensation", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117662A (13 March 2021); https://doi.org/10.1117/12.2591016

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