A Hybrid Scheme for Face Video Compression

With the rapid development of social media, the amount of face video data has grown rapidly, making face video compression a hot research topic. Traditional video coding techniques do not discriminate video content and compress all videos in the same way while talking head video compression should have more potential. Existing generative compression methods mostly adopt static reference frames, resulting in a decrease in fidelity caused by dynamic background or large pose change. In this paper, we propose a hybrid compression scheme for face videos which combines traditional coding with generative compression. On the one hand, we sample and encode key frames with traditional codecs to provide dynamic reference frames which contain real-time background and motion information. On the other hand, we devise a deep video generation model to synthesize smooth video frames according to the extracted sparse keypoints. Combining the pixel-level recovery capability of traditional coding with the detail generation capability of deep generative models, our proposed hybrid scheme is able to implement high-fidelity face video compression at low bitrate in real time. Additionally, we also devise a Portrait Recovery module to recover the low-quality key frames, improving the reconstruction quality in low-bitrate scenarios. Extensive experiments show that our method has advantages over traditional codecs and existing generative compression methods in terms of both rate-distortion performance and coding complexity.