RTFVE让普通电脑也能实时提升低码率视频画质。
RTFVE: Realtime Face Video Enhancement
- 基于轻量设计，可无缝集成到任意视频解码器中
- 在多种低码率下显著提升视觉质量，支持实时运行
- 适合远程会议场景，无需高端显卡
视频会议在个人与商业场景中广泛应用，但全球许多用户受限于带宽，难以获得理想体验。尽管深度学习可提升低码率视频质量，但现有模型通常难以兼容现代压缩标准，或需专用硬件如高性能GPU运行，实用性差。为此，我们提出实时人脸视频增强模型RTFVE，可轻松集成至任意视频解码器，并在普通CPU上实现实时运行。实验表明，在多个低码率设置下，该模型显著优于原始压缩视频的感知质量。源代码将公开于https://github.com/varun-jois/RTFVE。
原文摘要 · Abstract (English)
There's been a surge in adoption of video conferencing applications for both personal and business use cases. However, the bandwidth limitations faced by many users worldwide may restrict the optimal use of such applications. Although deep learning offers a solution for enhancing low bit rate videos, most models today are either hard to incorporate with modern compression standards or require specialized hardware to run such as significant GPUs making these models impractical. To address these issues, we introduce the Realtime Face Video Enhancement (RTFVE) model which can be easily incorporated with any video decoder and can run in realtime on ordinary CPUs. Experiments show that our model improves perceptual quality over the compressed video baseline at multiple low bitrate settings. The source code will be made available at https://github.com/varun-jois/RTFVE.
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