论文标题
通过使用多个神经网络来增强视频编码的内部预测
Enhanced Intra Prediction for Video Coding by Using Multiple Neural Networks
论文作者
论文摘要
本文通过使用多个神经网络模式（NM）增强了内部预测。每个NM用作从相邻参考块到当前编码块的端到端映射。对于提供的NMS，我们提出了两个方案（附加和替换），以将NMS与高效视频编码（HEVC）定义的传统模式（TM）集成在一起。对于附加方案，每个NM对应于一定范围的TMS。 TMS的分类基于预期的预测错误。在确定每个NM的相关TMS之后，我们提出概率感知模式信号传导方案。具有较高概率为最佳模式的NMS信号较少。对于替代方案，我们建议取代最高和最低的TMS。当替换最低可能的TMS时，还采用了新的最可能的模式（MPM）生成方法。实验结果表明，与单个NM相比，使用多个NM显然可以提高编码效率。具体而言，与在最先进的作品中使用单个NM相比，Y，U，V组件的提议的附加方案可以节省2.6％，3.8％，3.1％的BD率。
This paper enhances the intra prediction by using multiple neural network modes (NM). Each NM serves as an end-to-end mapping from the neighboring reference blocks to the current coding block. For the provided NMs, we present two schemes (appending and substitution) to integrate the NMs with the traditional modes (TM) defined in high efficiency video coding (HEVC). For the appending scheme, each NM is corresponding to a certain range of TMs. The categorization of TMs is based on the expected prediction errors. After determining the relevant TMs for each NM, we present a probability-aware mode signaling scheme. The NMs with higher probabilities to be the best mode are signaled with fewer bits. For the substitution scheme, we propose to replace the highest and lowest probable TMs. New most probable mode (MPM) generation method is also employed when substituting the lowest probable TMs. Experimental results demonstrate that using multiple NMs will improve the coding efficiency apparently compared with the single NM. Specifically, proposed appending scheme with seven NMs can save 2.6%, 3.8%, 3.1% BD-rate for Y, U, V components compared with using single NM in the state-of-the-art works.
