AQuA: A New Image Quality Metric for Optimizing Video Analytics Systems

Abstract

Millions of cameras at the edge are being deployed to power a variety of different deep learning applications. However, the frames captured by these cameras are not always pristine—they can be distorted due to lighting issues, sensor noise, compression etc. Such distortions not only deteriorate visual quality, they impact the accuracy of deep learning applications that process such video streams. In this work, we introduce AQuA, to protect application accuracy against such distorted frames by scoring the level of distortion in the frames. It takes into account the analytical quality of frames, not the visual quality, by learning a novel metric, classifier opinion score , and uses a lightweight, CNN-based, object-independent feature extractor. AQuA accurately scores distortion levels of frames and generalizes to multiple different deep learning applications. When used for filtering poor-quality frames at edge, it reduces high-confidence errors for analytics applications by 17%. Through filtering, and due to its low overhead (14 ms), AQuA can also reduce computation time and average bandwidth usage by 25%. Finally, we discuss numerous new avenues of optimizations of video analytics pipelines enabled by AQuA.