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.

翻译：自动驾驶系统需要广泛的数据采集方案，以覆盖构建鲁棒且安全系统所需的各种场景。数据量级可达艾字节（Exabytes），且需长期存储（即车辆生命周期超过10年）。无损压缩无法提供足够的压缩比，因此有损视频压缩技术已被探索。证明有损视频压缩伪影不影响感知算法性能至关重要。然而，该领域尚缺乏能得出可靠结论的研究工作。特别是针对具有高径向畸变且压缩可能产生更多伪影的鱼眼相机，至今尚无相关研究。鱼眼相机常用于汽车系统的3D物体检测任务。本研究首次分析了标准视频压缩编解码器对宽视场鱼眼相机图像的影响。我们证明：可实现的压缩比（对性能影响可忽略）取决于数据集与视频编解码器的时域预测能力。提出了一种基于径向畸变感知的分区度量方法，用于评估鱼眼图像中的伪影性能。此外，我们提出了一种估计最新VVC编解码器仿射模式参数的新方法，并为鱼眼图像应用的视频编解码器改进指明了若干方向。