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.

该研究提出了基于置信度的预测质量适应模块以及参考质量适应模块，以提高视频编码的预测和参考质量适应能力，并减少重构错误的传播。实验结果表明，在RGB和YUV420颜色空间中，我们的编码器比H.266/VVC参考软件和以前最先进的学习视频编码器获得更高的压缩性能。

学习视频压缩的预测和参考质量适应