RTFVE：实时人脸视频增强
RTFVE: Realtime Face Video Enhancement
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中文总结 AI 辅助
针对视频会议带宽受限及深度学习模型应用难题，提出RTFVE模型，可与视频解码器结合并在普通CPU实时运行，实验证明其在多低比特率设置下提升了压缩视频感知质量。
中文摘要 AI 辅助
视频会议应用在个人和商业场景中的使用激增，但全球许多用户面临的带宽限制可能会限制此类应用的最佳使用。深度学习虽能增强低比特率视频，但多数模型难以与现代压缩标准结合或需专用硬件。为此引入实时人脸视频增强（RTFVE）模型，它可轻松与任何视频解码器结合并在普通CPU上实时运行。实验表明，该模型在多个低比特率设置下提升了压缩视频基线的感知质量。
英文摘要
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.
发表机构
- Brandeis University（布兰迪斯大学）
机构由 AI 辅助整理，请以论文原文为准。
