论文标题
FullHD中基于介入的视频压缩
Inpainting-based Video Compression in FullHD
论文作者
论文摘要
基于含水层的压缩方法是静止图像的经典基于转换的编解码器的一种不断发展的替代方法。将这些想法应用于视频压缩的尝试很少，因为达到实时性能非常具有挑战性。因此，当前的方法集中于简化的逐框重建，这些重建忽略了时间冗余。作为一种补救措施，我们提出了一种高效，能够实时的预测和校正方法，该方法完全依赖于编解码器的所有步骤中的部分微分方程（PDE）：密集的变分光流场可准确地进行运动补偿的预测，而同质的扩散量应用于内在预测。为了压缩残留物，我们引入了一种新的高效基于块的基于伪差的变体。从质量和速度方面，我们的新颖体系结构的表现优于其他基于介绍的视频编解码器。我们可以在基于内部的视频压缩中首次实时地对FullHD（1080p）视频进行实时解压缩，并提供完全基于CPU的实现，以大约一个数量级的级数优于先前的方法。
Compression methods based on inpainting are an evolving alternative to classical transform-based codecs for still images. Attempts to apply these ideas to video compression are rare, since reaching real-time performance is very challenging. Therefore, current approaches focus on simplified frame-by-frame reconstructions that ignore temporal redundancies. As a remedy, we propose a highly efficient, real-time capable prediction and correction approach that fully relies on partial differential equations (PDEs) in all steps of the codec: Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting is applied for intra prediction. To compress residuals, we introduce a new highly efficient block-based variant of pseudodifferential inpainting. Our novel architecture outperforms other inpainting-based video codecs in terms of both quality and speed. For the first time in inpainting-based video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation, outperforming previous approaches by roughly one order of magnitude.
