In VP9 video codec, the sizes of blocks are decided during encoding by recursively partitioning $64\times 64$ superblocks using rate-distortion optimization (RDO). This process is computationally intensive because of the combinatorial search space of possible partitions of a superblock. Here, we propose a deep learning based alternative framework to predict the intra-mode superblock partitions in the form of a four-level partition tree, using a hierarchical fully convolutional network (H-FCN). We created a large database of VP9 superblocks and the corresponding partitions to train an H-FCN model, which was subsequently integrated with the VP9 encoder to reduce the intra-mode encoding time. The experimental results establish that our approach speeds up intra-mode encoding by 69.7% on average, at the expense of a 1.71% increase in the Bjøntegaard-Delta bitrate (BD-rate). While VP9 provides several built-in speed levels which are designed to provide faster encoding at the expense of decreased rate-distortion performance, we find that our model is able to outperform the fastest recommended speed level of the reference VP9 encoder for the good quality intra encoding configuration, in terms of both speedup and BD-rate.

中文翻译：

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通过基于分层深度学习的分区预测加速 VP9 帧内编码器。


在VP9视频编解码器中，块的大小在编码过程中通过递归分区来决定$64\乘64$使用率失真优化（RDO）的超级块。由于超级块的可能分区的组合搜索空间，该过程是计算密集型的。在这里，我们提出了一种基于深度学习的替代框架，使用分层全卷积网络（H-FCN）以四级分区树的形式预测内部模式超级块分区。我们创建了一个包含 VP9 超级块和相应分区的大型数据库来训练 H-FCN 模型，随后将其与 VP9 编码器集成以减少帧内模式编码时间。实验结果表明，我们的方法平均将帧内模式编码速度提高了 69.7%，但代价是 Bjøntegaard-Delta 比特率（BD 率）增加了 1.71%。虽然 VP9 提供了多个内置速度级别，旨在以降低率失真性能为代价提供更快的编码，但我们发现我们的模型能够超越参考 VP9 编码器的最快推荐速度级别好的在加速和 BD 速率方面的质量帧内编码配置。