论文标题
面部视频的生成压缩：混合方案
Generative Compression for Face Video: A Hybrid Scheme
论文作者
论文摘要
作为最新的视频编码标准，多功能视频编码（VVC）显示了其保持像素质量的能力。为了在超低比特率下为视频会议方案发掘更大的压缩潜力，本文提出了一个可调节面部视频的可调节混合压缩方案。这种混合方案将传统编码的像素级精确恢复能力与基于删节信息深度学习的生成能力结合在一起，在此信息中，Pixel Wise Wise Bi-Perdiction，低 - bItrate-fom和无损关键点编码器与PSNR合作以实现高达36.23 db的PSNR，高至1.47 kb/s。在不引入任何额外比特率的情况下，我们的方法在完全公平的比较实验下比VVC具有明显的优势，这证明了我们提出的方案的有效性。此外，我们的方案可以适应任何现有的编码器 /配置来处理不同的编码要求，并且可以根据网络条件动态调整比特率。
As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality. To excavate more compression potential for video conference scenarios under ultra-low bitrate, this paper proposes a bitrate adjustable hybrid compression scheme for face video. This hybrid scheme combines the pixel-level precise recovery capability of traditional coding with the generation capability of deep learning based on abridged information, where Pixel wise Bi-Prediction, Low-Bitrate-FOM and Lossless Keypoint Encoder collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s. Without introducing any additional bitrate, our method has a clear advantage over VVC under a completely fair comparative experiment, which proves the effectiveness of our proposed scheme. Moreover, our scheme can adapt to any existing encoder / configuration to deal with different encoding requirements, and the bitrate can be dynamically adjusted according to the network condition.
