  While the performance of recent learned intra and sequential video
compression models exceed that of respective traditional codecs, the
performance of learned B-frame compression models generally lag behind
traditional B-frame coding. The performance gap is bigger for complex scenes
with large motions. This is related to the fact that the distance between the
past and future references vary in hierarchical B-frame compression depending
on the level of hierarchy, which causes motion range to vary. The inability of
a single B-frame compression model to adapt to various motion ranges causes
loss of performance. As a remedy, we propose controlling the motion range for
flow prediction during inference (to approximately match the range of motions
in the training data) by downsampling video frames adaptively according to
amount of motion and level of hierarchy in order to compress all B-frames using
a single flexible-rate model. We present state-of-the-art BD rate results to
demonstrate the superiority of our proposed single-model motion-adaptive
inference approach to all existing learned B-frame compression models.
