Inpainting-Based Video Compression in FullHD

Compression methods based on inpainting have been an active field of research in the past decade. Videos are especially challenging for this kind of methods, since real-time decompression requires highly efficient algorithms. Dedicated inpainting-based video codecs have so far focused on efficient frame-by-frame reconstruction without exploiting redundancies in time. As a remedy, we propose a modular framework that combines a classical prediction and correction approach with suitable structures for fully inpainting-based methods. The core idea of these techniques is to store values only at a small number of positions, and reconstruct missing regions via inpainting. Our generic framework supports any algorithm that generates such sparse representations. As a concrete demonstrator, we provide a prototypical implementation of our framework by supplementing all modules with methods based on partial differential equations (PDEs): Dense variational optic flow fields yield accurate motion-compensated predictions, while homogeneous diffusion inpainting and pseudodifferential equations are applied as intra prediction and residual compression techniques. With these components, we are able to outperform other inpainting-based video codecs in terms of 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.