  Significant advances in video compression system have been made in the past
several decades to satisfy the nearly exponential growth of Internet-scale
video traffic. From the application perspective, we have identified three major
functional blocks including pre-processing, coding, and post-processing, that
have been continuously investigated to maximize the end-user quality of
experience (QoE) under a limited bit rate budget. Recently, artificial
intelligence (AI) powered techniques have shown great potential to further
increase the efficiency of the aforementioned functional blocks, both
individually and jointly. In this article, we review extensively recent
technical advances in video compression system, with an emphasis on deep neural
network (DNN)-based approaches; and then present three comprehensive case
studies. On pre-processing, we show a switchable texture-based video coding
example that leverages DNN-based scene understanding to extract semantic areas
for the improvement of subsequent video coder. On coding, we present an
end-to-end neural video coding framework that takes advantage of the stacked
DNNs to efficiently and compactly code input raw videos via fully data-driven
learning. On post-processing, we demonstrate two neural adaptive filters to
respectively facilitate the in-loop and post filtering for the enhancement of
compressed frames. Finally, a companion website hosting the contents developed
in this work can be accessed publicly at
https://purdueviper.github.io/dnn-coding/.
