Neural Pixel Error Detection

Current video quality control entails a manual review of every frame for every video for pixel errors. A pixel error is a single or small group of anomalous pixels displaying incorrect colors, arising from multiple sources in the video production pipeline. The detection process is difficult, time consuming, and rife with human error. In this work, we present a novel approach for automated pixel error detection, applying simple machine learning techniques to great effect. We use an autoencoder architecture followed by statistical post-processing to catch all tested live action pixel anomalies while keeping the false positive rate to a minimum. We discuss previous dead pixel detection methods in image processing, and compare to other machine learning approaches.