This paper proposes a Perceptual Learned Video Compression (PLVC) approach
with recurrent conditional GAN. We employ the recurrent auto-encoder-based
compression network as the generator, and most importantly, we propose a
recurrent conditional discriminator, which judges on raw vs. compressed video
conditioned on both spatial and temporal features, including the latent
representation, temporal motion and hidden states in recurrent cells. This way,
the adversarial training pushes the generated video to be not only spatially
photo-realistic but also temporally consistent with the groundtruth and
coherent among video frames. The experimental results show that the learned
PLVC model compresses video with good perceptual quality at low bit-rate, and
that it outperforms the official HEVC test model (HM 16.20) and the existing
learned video compression approaches for several perceptual quality metrics and
user studies. The codes will be released at the project page:
this https URL

本文提出了一种适用于逐帧视频的感知式学习视频压缩 (Pervetual Learned Video Compression，PLVC) 方法，并首次提出了一种递归条件判别器，该方法可以在保留视频画质和压缩率同时，满足时域上的一致性，而且其性能远远好于现有的学习视频压缩算法及现有标准 HEVC HM 16.20。