No abstract

No abstract

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 motioncompensated 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 inpaintingbased video compression, we can decompress FullHD (1080p) videos in real-time with a fully CPU-based implementation.

Inpainting-based compression represents images in terms of a sparse subset of its pixel data. Storing the carefully optimised positions of known data creates a lossless compression problem on sparse and often scattered binary images. This central issue is crucial for the performance of such codecs. Since it has only received little attention in the literature, we conduct the first systematic investigation of this problem so far. To this end, we first review and compare a wide range of existing methods from image compression and general purpose coding in terms of their coding efficiency and runtime. Afterwards, an ablation study enables us to identify and isolate the most useful components of existing methods. With context mixing, we combine those ingredients into new codecs that offer either better compression ratios or a more favourable trade-off between speed and performance.