In this paper, we present a novel adversarial lossy video compression model.
At extremely low bit-rates, standard video coding schemes suffer from
unpleasant reconstruction artifacts such as blocking, ringing etc. Existing
learned neural approaches to video compression have achieved reasonable success
on reducing the bit-rate for efficient transmission and reduce the impact of
artifacts to an extent. However, they still tend to produce blurred results
under extreme compression. In this paper, we present a deep adversarial learned
video compression model that minimizes an auxiliary adversarial distortion
objective. We find this adversarial objective to correlate better with human
perceptual quality judgement relative to traditional quality metrics such as
MS-SSIM and PSNR. Our experiments using a state-of-the-art learned video
compression system demonstrate a reduction of perceptual artifacts and
reconstruction of detail lost especially under extremely high compression.

本文提出了一种新颖的深度对抗学习视频压缩模型，该模型采用一个辅助的对抗性失真目标函数，我们将该目标函数与传统的质量指标如 MS-SSIM 和 PSNR 相比较，发现该对抗目标函数更符合人类感知质量判断标准，实验结果证明该模型能够在极高的压缩率下减少感知失真和重建丢失的细节。