Fifteen months ago, MPAI started an investigation on AI-based End-to-End Video Coding, a new approach is not based on traditional video coding architectures. Recently published results from the investigation show that Version 0.3 of the MPAI-EEV Reference Model has generally higher performance than the MPEG-HEVC video coding standard when applied to the MPAI set of high-quality drone video sequences.
MPAI is now offering its Unmanned Aerial Vehicle (UAV) sequence dataset for use by the video community in testing compression algorithms. The dataset contains various drone videos captured under different conditions, including environments, flight altitudes, and camera views. These video clips are selected from several categories of real-life objects in different scene object densities and lighting conditions, representing diverse scenarios in our daily life.
Compared to natural videos, UAV-captured videos are generally recorded by drone-mounted cameras in motion and at different viewpoints and altitudes. These features bring several new challenges, such as motion blur, scale changes and complex background. Heavy occlusion, non-rigid deformation and tiny scales of objects might be of great challenge to drone video compression.
Please get an invitation from the MPAI Secretariat and come to one of the biweekly meetings of the MPAI-EEV group (starting from 1st of February 2023). The MPAI-EEV group is going to showcase its superior performance fully neural network-based video codec model for drone videos. The group is inclusive and planning for the future of video coding using end-to-end learning. Please feel free to participate, leaving your comments or suggestions to the MPAI-EEV. We will discuss your contribution and our state of the art with the goal of progressing this exciting area of coding of video sequences from drones.
Table 1 – Drone video test sequences
Class A VisDrone-SOT TPAM12021
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