Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis
This work addresses the problem of efficient video storage and transmission for drone platforms, but it is incremental as it focuses on benchmarking rather than introducing a new method.
The paper tackles the challenge of compressing UAV-captured video, which has unique texture and view characteristics not addressed by existing compression schemes, by establishing a benchmark for learned video coding and comparing learned and conventional codecs in terms of rate-distortion efficiency.
During the past decade, the Unmanned-Aerial-Vehicles (UAVs) have attracted increasing attention due to their flexible, extensive, and dynamic space-sensing capabilities. The volume of video captured by UAVs is exponentially growing along with the increased bitrate generated by the advancement of the sensors mounted on UAVs, bringing new challenges for on-device UAV storage and air-ground data transmission. Most existing video compression schemes were designed for natural scenes without consideration of specific texture and view characteristics of UAV videos. In this work, we first contribute a detailed analysis of the current state of the field of UAV video coding. Then we propose to establish a novel task for learned UAV video coding and construct a comprehensive and systematic benchmark for such a task, present a thorough review of high quality UAV video datasets and benchmarks, and contribute extensive rate-distortion efficiency comparison of learned and conventional codecs after. Finally, we discuss the challenges of encoding UAV videos. It is expected that the benchmark will accelerate the research and development in video coding on drone platforms.