In this paper, we investigate video analytics in low-light environments, and propose an end-edge coordinated system with joint video encoding and enhancement. It adaptively transmits low-light videos from cameras and performs enhancement and inference tasks at the edge. Firstly, according to our observations, both encoding and enhancement for low-light videos have a significant impact on inference accuracy, which directly influences bandwidth and computation overhead. Secondly, due to the limitation of built-in computation resources, cameras perform encoding and transmitting frames to the edge. The edge executes neural enhancement to process low contrast, detail loss, and color distortion on low-light videos before inference. Finally, an adaptive controller is designed at the edge to select quantization parameters and scales of neural enhancement networks, aiming to improve the inference accuracy and meet the latency requirements. Extensive real-world experiments demon-strate that, the proposed system can achieve a better trade-off between communication and computation resources and optimize the inference accuracy.

翻译：本文研究了低光照环境下的视频分析问题，提出了一种端边协同的视频联合编码与增强系统。该系统自适应地传输摄像头采集的低光照视频，并在边缘端执行增强与推理任务。首先，我们的观察表明：低光照视频的编码与增强过程对推理精度具有显著影响，进而直接决定带宽与计算开销。其次，受限于内置计算资源，摄像头仅负责编码与帧传输，而边缘端则在推理前对低光照视频执行神经增强处理，以解决对比度不足、细节丢失及色彩失真等问题。最后，我们在边缘端设计了自适应控制器，用于选择神经网络增强网络的量化参数与尺度，旨在提升推理精度并满足时延约束。大量真实世界实验证明，所提系统能够实现通信与计算资源间的更优权衡，并优化推理精度。