End-Edge Coordinated Joint Encoding and Neural Enhancement for Low-Light Video Analytics

TL;DR

This paper presents an end-edge system for low-light video analytics that adaptively encodes, enhances, and infers on videos to optimize accuracy and resource use in real-world scenarios.

Contribution

It introduces a joint encoding and neural enhancement framework with an adaptive controller for low-light video processing at the edge, improving inference accuracy and resource efficiency.

Findings

Enhanced inference accuracy in low-light videos

Reduced bandwidth and computation overhead

Achieved better resource trade-offs in real-world tests

Abstract

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.