  The rapid advancement of artificial intelligence (AI) technology has led to
the prioritization of standardizing the processing, coding, and transmission of
video using neural networks. To address this priority area, the Moving Picture,
Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a
suite of standards called MPAI-EEV for "end-to-end optimized neural video
coding." The aim of this AI-based video standard project is to compress the
number of bits required to represent high-fidelity video data by utilizing
data-trained neural coding technologies. This approach is not constrained by
how data coding has traditionally been applied in the context of a hybrid
framework. This paper presents an overview of recent and ongoing
standardization efforts in this area and highlights the key technologies and
design philosophy of EEV. It also provides a comparison and report on some
primary efforts such as the coding efficiency of the reference model.
Additionally, it discusses emerging activities such as learned
Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under
development, or in the exploration phase. With a focus on UAV video signals,
this paper addresses the current status of these preliminary efforts. It also
indicates development timelines, summarizes the main technical details, and
provides pointers to further points of reference. The exploration experiment
shows that the EEV model performs better than the state-of-the-art video coding
standard H.266/VVC in terms of perceptual evaluation metric.
