We study causal, low-latency, sequential video compression when the output is
subjected to both a mean squared-error (MSE) distortion loss as well as a
perception loss to target realism. Motivated by prior approaches, we consider
two different perception loss functions (PLFs). The first, PLF-JD, considers
the joint distribution (JD) of all the video frames up to the current one,
while the second metric, PLF-FMD, considers the framewise marginal
distributions (FMD) between the source and reconstruction. Using information
theoretic analysis and deep-learning based experiments, we demonstrate that the
choice of PLF can have a significant effect on the reconstruction, especially
at low-bit rates. In particular, while the reconstruction based on PLF-JD can
better preserve the temporal correlation across frames, it also imposes a
significant penalty in distortion compared to PLF-FMD and further makes it more
difficult to recover from errors made in the earlier output frames. Although
the choice of PLF decisively affects reconstruction quality, we also
demonstrate that it may not be essential to commit to a particular PLF during
encoding and the choice of PLF can be delegated to the decoder. In particular,
encoded representations generated by training a system to minimize the MSE
(without requiring either PLF) can be {\em near universal} and can generate
close to optimal reconstructions for either choice of PLF at the decoder. We
validate our results using (one-shot) information-theoretic analysis, detailed
study of the rate-distortion-perception tradeoff of the Gauss-Markov source
model as well as deep-learning based experiments on moving MNIST and KTH
datasets.

研究了在输出同时受到均方差失真损失和感知损失的影响下进行因果、低延迟、连续视频压缩的方法，证明了 PLF 的选择会显著影响图像重建，特别是在低比特率情况下，在编码时委托 PLF 的选择给解码器会更加有效，结果使用信息论分析和深度学习验证。

关于学习视频压缩的感知损失函数选择

On the Choice of Perception Loss Function for Learned Video Compression

We present a lossy image compression method based on deep convolutional
neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and
JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same
bit rate. Currently, most of the CNNs based approaches train the network using
a L2 loss between the reconstructions and the ground-truths in the pixel
domain, which leads to over-smoothing results and visual quality degradation
especially at a very low bit rate. Therefore, we improve the subjective quality
with the combination of a perception loss and an adversarial loss additionally.
To achieve better rate-distortion optimization (RDO), we also introduce an
easy-to-hard transfer learning when adding quantization error and rate
constraint. Finally, we evaluate our method on public Kodak and the Test
Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in
averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.

使用深度卷积神经网络的有损图像压缩方法，结合感知损失和对抗损失以及逐渐引入量化误差和速率约束的易到难迁移学习实现更好的速率 - 失真优化。在多个数据集中，相比于 BPG，WebP，JPEG2000 和 JPEG，平均达到了 7.81％ - 19.1％的 BD - 率降低。