  Temporal prediction is one of the most important technologies for video
compression. Various prediction coding modes are designed in traditional video
codecs. Traditional video codecs will adaptively to decide the optimal coding
mode according to the prediction quality and reference quality. Recently,
learned video codecs have made great progress. However, they ignore the
prediction and reference quality adaptation, which leads to incorrect
utilization of temporal prediction and reconstruction error propagation.
Therefore, in this paper, we first propose a confidence-based prediction
quality adaptation (PQA) module to provide explicit discrimination for the
spatial and channel-wise prediction quality difference. With this module, the
prediction with low quality will be suppressed and that with high quality will
be enhanced. The codec can adaptively decide which spatial or channel location
of predictions to use. Then, we further propose a reference quality adaptation
(RQA) module and an associated repeat-long training strategy to provide dynamic
spatially variant filters for diverse reference qualities. With the filters, it
is easier for our codec to achieve the target reconstruction quality according
to reference qualities, thus reducing the propagation of reconstruction errors.
Experimental results show that our codec obtains higher compression performance
than the reference software of H.266/VVC and the previous state-of-the-art
learned video codecs in both RGB and YUV420 colorspaces.
