论文标题
对对抗性的视频压缩的对抗失真
Adversarial Distortion for Learned Video Compression
论文作者
论文摘要
在本文中,我们提出了一种新颖的对抗性有损视频压缩模型。在极低的位速率下,标准的视频编码方案遭受了令人不快的重建工件,例如阻塞,振铃等。现有的学到的视频压缩神经方法在减少有效传输的位尺度方面取得了合理的成功,并在一定程度上减少了工件的影响。但是,它们仍然倾向于在极端压缩下产生模糊的结果。在本文中,我们提出了一个深厚的对抗性学习的视频压缩模型,该模型将辅助对手失真目标最小化。我们发现,相对于传统质量指标(例如MS-SSIM和PSNR),与人类感知质量判断更好地相关的对抗性目标。我们使用最先进的视频压缩系统进行的实验表明,在极高的压缩下,尤其是在极高的压缩下损失的细节的感知伪像减少。
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.