  Video coding, which targets to compress and reconstruct the whole frame, and
feature compression, which only preserves and transmits the most critical
information, stand at two ends of the scale. That is, one is with compactness
and efficiency to serve for machine vision, and the other is with full
fidelity, bowing to human perception. The recent endeavors in imminent trends
of video compression, e.g. deep learning based coding tools and end-to-end
image/video coding, and MPEG-7 compact feature descriptor standards, i.e.
Compact Descriptors for Visual Search and Compact Descriptors for Video
Analysis, promote the sustainable and fast development in their own directions,
respectively. In this paper, thanks to booming AI technology, e.g. prediction
and generation models, we carry out exploration in the new area, Video Coding
for Machines (VCM), arising from the emerging MPEG standardization efforts1.
Towards collaborative compression and intelligent analytics, VCM attempts to
bridge the gap between feature coding for machine vision and video coding for
human vision. Aligning with the rising Analyze then Compress instance Digital
Retina, the definition, formulation, and paradigm of VCM are given first.
Meanwhile, we systematically review state-of-the-art techniques in video
compression and feature compression from the unique perspective of MPEG
standardization, which provides the academic and industrial evidence to realize
the collaborative compression of video and feature streams in a broad range of
AI applications. Finally, we come up with potential VCM solutions, and the
preliminary results have demonstrated the performance and efficiency gains.
Further direction is discussed as well.
