# Inpainting-based Video Compression in FullHD

arXiv: https://arxiv.org/abs/2008.10273 · HTML: https://mlsanity.com/paper/2008.10273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Learning Sparse Masks for Diffusion-based Image Inpainting](https://mlsanity.com/paper/2110.02636/) (2021)
- [Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields](https://mlsanity.com/paper/2405.14599/) (2024)

## Abstract (arXiv)

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.