In this paper we combine video compression and modern image processing methods. We construct novel iterative filter methods for prediction signals based on Partial Differential Equation (PDE) based methods. The mathematical framework of the employed diffusion filter class is given and some desirable properties are stated. In particular, two types of diffusion filters are constructed: a uniform diffusion filter using a fixed filter mask and a signal adaptive diffusion filter that incorporates the structures of the underlying prediction signal. The latter has the advantage of not attenuating existing edges while the uniform filter is less complex. The filters are embedded into a software based on HEVC with additional QTBT (Quadtree plus Binary Tree) and MTT (Multi-Type-Tree) block structure. In this setting, several measures to reduce the coding complexity of the tool are introduced, discussed and tested thoroughly. The coding complexity is reduced by up to 70% while maintaining over 80% of the gain. Overall, the diffusion filter method achieves average bitrate savings of 2.27% for Random Access having an average encoder runtime complexity of 119% and 117% decoder runtime complexity. For individual test sequences, results of 7.36% for Random Access are accomplished.

在本文中，我们结合了视频压缩和现代图像处理方法。我们基于偏微分方程（PDE）的方法构造了用于预测信号的新型迭代滤波器方法。给出了所采用的扩散滤波器类别的数学框架，并陈述了一些理想的特性。特别地，构造了两种类型的扩散滤波器：使用固定滤波器掩模的均匀扩散滤波器和结合了基础预测信号的结构的信号自适应扩散滤波器。后者的优点是不衰减现有边缘，而均匀滤波器的复杂性较低。过滤器被嵌入到基于HEVC的软件中，该软件具有附加的QTBT（四叉树加二叉树）和MTT（多类型树）块结构。在这种情况下，全面介绍，讨论和测试了降低工具编码复杂度的几种措施。编码复杂度降低了70％，同时保持了80％以上的增益。总体而言，对于平均访问速度为119％，解码器运行时间复杂度为11％的随机访问的随机访问，扩散滤波器方法可节省2.27％的平均比特率。对于单个测试序列，随机访问的结果为7.36％。