Patney, AnjulLefohn, AaronPatney, Anjul and Niessner, Matthias2018-11-112018-11-112018978-1-4503-5896-52079-8679https://doi.org/10.1145/3231578.3231580https://diglib.eg.org:443/handle/10.1145/3231578-3231580In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported from a real-time renderer. The trained network learns to identify and locate aliasing artifacts in an input sequence, without comparing it against a ground truth. Thus, it is useful as a fully automated tool for evaluating image quality. We demonstrate the effectiveness of our approach in detecting aliasing in several rendered sequences. The trained network correctly predicts aliasing in 64×64×4 animated sequences with more than 90% accuracy for images it hasn't seen before. The output of our network is a single scalar between 0 and 1, which is usable as a quality metric for aliasing. It follows the same trend as (1-SSIM) for images with increasing sample counts.Computing methodologiesAntialiasingMachine learningaliasingvisual quality assessmentmachine learning10.1145/3231578.3231580