  In recent years, the image and video coding technologies have advanced by
leaps and bounds. However, due to the popularization of image and video
acquisition devices, the growth rate of image and video data is far beyond the
improvement of the compression ratio. In particular, it has been widely
recognized that there are increasing challenges of pursuing further coding
performance improvement within the traditional hybrid coding framework. Deep
convolution neural network (CNN) which makes the neural network resurge in
recent years and has achieved great success in both artificial intelligent and
signal processing fields, also provides a novel and promising solution for
image and video compression. In this paper, we provide a systematic,
comprehensive and up-to-date review of neural network based image and video
compression techniques. The evolution and development of neural network based
compression methodologies are introduced for images and video respectively.
More specifically, the cutting-edge video coding techniques by leveraging deep
learning and HEVC framework are presented and discussed, which promote the
state-of-the-art video coding performance substantially. Moreover, the
end-to-end image and video coding frameworks based on neural networks are also
reviewed, revealing interesting explorations on next generation image and video
coding frameworks/standards. The most significant research works on the image
and video coding related topics using neural networks are highlighted, and
future trends are also envisioned. In particular, the joint compression on
semantic and visual information is tentatively explored to formulate high
efficiency signal representation structure for both human vision and machine
vision, which are the two dominant signal receptor in the age of artificial
intelligence.
