High dynamic range (HDR) scenes are known to be challenging for most cameras. The most common artifacts associated with bad HDR scene rendition are clipped bright areas and noisy dark regions, rendering the images unnatural and unpleasing. This paper introduces a novel methodology for automating the perceptual evaluation of detail rendition in these extreme regions of the histogram for images that portray natural scenes. The key contributions include 1) the construction of a robust database in Just Objectionable Distance (JOD) scores, incorporating annotator outlier detection 2) the introduction of a Multitask Convolutional Neural Network (CNN) model that effectively addresses the diverse context and region-of-interest challenges inherent in natural scenes. Our experimental evaluation demonstrates that our approach strongly aligns with human evaluations. The adaptability of our model positions it as a valuable tool for ensuring consistent camera performance evaluation, contributing to the continuous evolution of smartphone technologies.
Gabriel Pacianotto, Daniela Carfora, Franck Xu, Sira Ferradans, Benoit Pochon, "Evaluation of Bright and Dark Details in HDR Scenes: A Multitask CNN Approach" in London Imaging Meeting, 2024, pp 6 - 11, https://doi.org/10.2352/lim.2024.5.1.2