In this paper, we tackle the issue of estimating the noise level of a camera, on its processed still images and as perceived by the user. Commonly, the characterization of the noise level of a camera is done using objective metrics determined on charts containing uniform patches at a given condition. These methods can lead to inadequate characterizations of the noise of a camera because cameras often incorporate denoising algorithms that are more efficient on uniform areas than on areas containing details. Therefore, in this paper, we propose a method to estimate the perceived noise level on natural areas of a still-life chart. Our method is based on a deep convolutional network trained with ground truth quality scores provided by expert annotators. Our experimental evaluation shows that our approach strongly matches human evaluations.
Salim Belkarfa, Ahmed Hakim Choukarah, Marcelin Tworski, "Automatic Noise Analysis on Still Life Chart" in Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning, 2021, pp 101 - 105, https://doi.org/10.2352/issn.2694-118X.2021.LIM-101