Social media, self-driving cars, and traffic cameras produce video streams at
large scales and cheap cost. However, storing and querying video at such scales
is prohibitively expensive. We propose to treat large-scale video analytics as
a data warehousing problem: Video is a format that is easy to produce but needs
to be transformed into an application-specific format that is easy to query.
Analogously, we define the problem of Video Extract-Transform-Load (V-ETL).
V-ETL systems need to reduce the cost of running a user-defined V-ETL job while
also giving throughput guarantees to keep up with the rate at which data is
produced. We find that no current system sufficiently fulfills both needs and
therefore propose Skyscraper, a system tailored to V-ETL. Skyscraper can
execute arbitrary video ingestion pipelines and adaptively tunes them to reduce
cost at minimal or no quality degradation, e.g., by adjusting sampling rates
and resolutions to the ingested content. Skyscraper can hereby be provisioned
with cheap on-premises compute and uses a combination of buffering and cloud
bursting to deal with peaks in workload caused by expensive processing
configurations. In our experiments, we find that Skyscraper significantly
reduces the cost of V-ETL ingestion compared to adaptions of current SOTA
systems, while at the same time giving robustness guarantees that these systems
are lacking.

视频分析与处理的数据仓库问题：通过自定义的视频提取 - 转换 - 加载（V-ETL）系统，降低大规模视频处理的成本，并保持高吞吐率。

视频流的抽取 - 转换 - 加载

Extract-Transform-Load for Video Streams

We present a solution for the goal of extracting a video from a single motion
blurred image to sequentially reconstruct the clear views of a scene as beheld
by the camera during the time of exposure. We first learn motion representation
from sharp videos in an unsupervised manner through training of a convolutional
recurrent video autoencoder network that performs a surrogate task of video
reconstruction. Once trained, it is employed for guided training of a motion
encoder for blurred images. This network extracts embedded motion information
from the blurred image to generate a sharp video in conjunction with the
trained recurrent video decoder. As an intermediate step, we also design an
efficient architecture that enables real-time single image deblurring and
outperforms competing methods across all factors: accuracy, speed, and
compactness. Experiments on real scenes and standard datasets demonstrate the
superiority of our framework over the state-of-the-art and its ability to
generate a plausible sequence of temporally consistent sharp frames.

设计了一个端到端的自动编码器网络来提取运动模糊图像中的视频信息，生成具有时间上连续性的清晰图片序列，同时实现了实时的单张图像去模糊操作，结果表明我们的方法在精度、速度和紧凑性方面均优于现有方法。