  Millions of cameras at 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 (14ms), AQuA can also reduce computation
time and average bandwidth usage by 25%.
