Existing video compression (VC) methods primarily aim to reduce the spatial
and temporal redundancies between consecutive frames in a video while
preserving its quality. In this regard, previous works have achieved remarkable
results on videos acquired under specific settings such as instant (known)
exposure time and shutter speed which often result in sharp videos. However,
when these methods are evaluated on videos captured under different temporal
priors, which lead to degradations like motion blur and low frame rate, they
fail to maintain the quality of the contents. In this work, we tackle the VC
problem in a general scenario where a given video can be blurry due to
predefined camera settings or dynamics in the scene. By exploiting the natural
trade-off between visual enhancement and data compression, we formulate VC as a
min-max optimization problem and propose an effective framework and training
strategy to tackle the problem. Extensive experimental results on several
benchmark datasets confirm the effectiveness of our method compared to several
state-of-the-art VC approaches.

在这项研究中，我们通过利用视觉增强和数据压缩之间的自然权衡，将视频压缩问题定式为一个最小最大化优化问题，并提出了一个有效的框架和训练策略来解决该问题。实验结果表明，我们的方法在多个基准数据集上比多个最先进的视频压缩方法更为有效。

模糊视频压缩：在视觉增强和数据压缩之间的权衡

Blurry Video Compression: A Trade-off between Visual Enhancement and  Data Compression

Space-time video super-resolution (STVSR) is the task of interpolating videos
with both Low Frame Rate (LFR) and Low Resolution (LR) to produce
High-Frame-Rate (HFR) and also High-Resolution (HR) counterparts. The existing
methods based on Convolutional Neural Network~(CNN) succeed in achieving
visually satisfied results while suffer from slow inference speed due to their
heavy architectures. We propose to resolve this issue by using a
spatial-temporal transformer that naturally incorporates the spatial and
temporal super resolution modules into a single model. Unlike CNN-based
methods, we do not explicitly use separated building blocks for temporal
interpolations and spatial super-resolutions; instead, we only use a single
end-to-end transformer architecture. Specifically, a reusable dictionary is
built by encoders based on the input LFR and LR frames, which is then utilized
in the decoder part to synthesize the HFR and HR frames. Compared with the
state-of-the-art TMNet \cite{xu2021temporal}, our network is $60\%$ smaller
(4.5M vs 12.3M parameters) and $80\%$ faster (26.2fps vs 14.3fps on
$720\times576$ frames) without sacrificing much performance. The source code is
available at this https URL

提出了一种基于空间 - 时间变换器的方法来解决空间 - 时间视频超分辨率问题，相比于基于卷积神经网络的方法，这种方法不需要明确使用单独的构建模块进行时间插值和空间超分辨率，而是只使用一个端到端的转换器架构，并且具有更快的推理速度和更少的参数。