Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis

TL;DR

This paper provides a comprehensive analysis and benchmark of UAV video compression techniques, highlighting the unique challenges and proposing a systematic evaluation framework for learned and conventional codecs.

Contribution

It introduces a new benchmark for UAV video coding, reviews existing datasets, and compares the performance of learned versus traditional codecs.

Findings

Learned codecs show promising rate-distortion efficiency.

UAV videos have unique texture and view characteristics affecting compression.

Benchmark accelerates UAV video coding research.

Abstract

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.