  Existing learned video compression models employ flow net or deformable
convolutional networks (DCN) to estimate motion information. However, the
limited receptive fields of flow net and DCN inherently direct their
attentiveness towards the local contexts. Global contexts, such as large-scale
motions and global correlations among frames are ignored, presenting a
significant bottleneck for capturing accurate motions. To address this issue,
we propose a joint local and global motion compensation module (LGMC) for
leaned video coding. More specifically, we adopt flow net for local motion
compensation. To capture global context, we employ the cross attention in
feature domain for motion compensation. In addition, to avoid the quadratic
complexity of vanilla cross attention, we divide the softmax operations in
attention into two independent softmax operations, leading to linear
complexity. To validate the effectiveness of our proposed LGMC, we integrate it
with DCVC-TCM and obtain learned video compression with joint local and global
motion compensation (LVC-LGMC). Extensive experiments demonstrate that our
LVC-LGMC has significant rate-distortion performance improvements over baseline
DCVC-TCM.
