Measuring Noticeability: Multi-scale Context Aggregation for Prioritizing Video Anomalies.

Determining how impactful an anomaly is on the viewing experience of an audience is important to production studios, content creators, and content distributors. However, judging the impact of each anomaly is a highly manual and subjective task. To support automation of this, we propose the use of noticeability and introduce a method of prediction. We employed a psychophysical experimental method to capture the impact of various anomalies across various images. We then developed a multi-scale context aggregation model, trained on that data, to predict the noticeability of anomalies on novel images. This noticeability prediction can then be used to prioritize anomalies and ensure that remediation efforts are spent where it would most benefit the audience experience.