  Using more reference frames can significantly improve the compression
efficiency in neural video compression. However, in low-latency scenarios, most
existing neural video compression frameworks usually use the previous one frame
as reference. Or a few frameworks which use the previous multiple frames as
reference only adopt a simple multi-reference frames propagation mechanism. In
this paper, we present a more reasonable multi-reference frames propagation
mechanism for neural video compression, called butterfly multi-reference frame
propagation mechanism (Butterfly), which allows a more effective feature fusion
of multi-reference frames. By this, we can generate more accurate temporal
context conditional prior for Contextual Coding Module. Besides, when the
number of decoded frames does not meet the required number of reference frames,
we duplicate the nearest reference frame to achieve the requirement, which is
better than duplicating the furthest one. Experiment results show that our
method can significantly outperform the previous state-of-the-art (SOTA), and
our neural codec can achieve -7.6% bitrate save on HEVC Class D dataset when
compares with our base single-reference frame model with the same compression
configuration.
