  Learned hierarchical B-frame coding aims to leverage bi-directional reference
frames for better coding efficiency. However, the domain shift between training
and test scenarios due to dataset limitations poses a challenge. This issue
arises from training the codec with small groups of pictures (GOP) but testing
it on large GOPs. Specifically, the motion estimation network, when trained on
small GOPs, is unable to handle large motion at test time, incurring a negative
impact on compression performance. To mitigate the domain shift, we present an
online motion resolution adaptation (OMRA) method. It adapts the spatial
resolution of video frames on a per-frame basis to suit the capability of the
motion estimation network in a pre-trained B-frame codec. Our OMRA is an
online, inference technique. It need not re-train the codec and is readily
applicable to existing B-frame codecs that adopt hierarchical bi-directional
prediction. Experimental results show that OMRA significantly enhances the
compression performance of two state-of-the-art learned B-frame codecs on
commonly used datasets.
