Impact of Video Compression Artifacts on Fisheye Camera Visual
  Perception Tasks
Madhumitha Sakthi Louis Kerofsky Varun Ravi Kumar Senthil Yogamani
Autonomous driving systems require extensive data collection schemes to cover
the diverse scenarios needed for building a robust and safe system. The data
volumes are in the order of Exabytes and have to be stored for a long period of
time (i.e., more than 10 years of the vehicle's life cycle). Lossless
compression doesn't provide sufficient compression ratios, hence, lossy video
compression has been explored. It is essential to prove that lossy video
compression artifacts do not impact the performance of the perception
algorithms. However, there is limited work in this area to provide a solid
conclusion. In particular, there is no such work for fisheye cameras, which
have high radial distortion and where compression may have higher artifacts.
Fisheye cameras are commonly used in automotive systems for 3D object detection
task. In this work, we provide the first analysis of the impact of standard
video compression codecs on wide FOV fisheye camera images. We demonstrate that
the achievable compression with negligible impact depends on the dataset and
temporal prediction of the video codec. We propose a radial distortion-aware
zonal metric to evaluate the performance of artifacts in fisheye images. In
addition, we present a novel method for estimating affine mode parameters of
the latest VVC codec, and suggest some areas for improvement in video codecs
for the application to fisheye imagery.