  In recent years, visual sensors have been quickly improving towards mimicking
the visual information acquisition process of human brain, by responding to
illumination changes as they occur in time rather than at fixed time intervals.
In this context, the so-called neuromorphic vision sensors depart from the
conventional frame-based image sensors by adopting a paradigm shift in the way
visual information is acquired. This new way of visual information acquisition
enables faster and asynchronous per-pixel responses/recordings driven by the
scene dynamics with a very high dynamic range and low power consumption.
However, the huge amount of data outputted by the emerging neuromorphic vision
sensors critically demands highly efficient coding solutions in order
applications may take full advantage of these new, attractive sensors'
capabilities. For this reason, considerable research efforts have been invested
in recent years towards developing increasingly efficient neuromorphic vision
data coding (NVDC) solutions. In this context, the main objective of this paper
is to provide a comprehensive overview of NVDC solutions in the literature,
guided by a novel classification taxonomy, which allows better organizing this
emerging field. In this way, more solid conclusions can be drawn about the
current NVDC status quo, thus allowing to better drive future research and
standardization developments in this emerging technical area.
