TY  - STD
AU  - Justine Giroux
AU  - Mohammad Reza Karimi Dastjerdi
AU  - Yannick Hold-Geoffroy
AU  - Javier Vazquez
AU  - Jean François Lalonde
PY  - 2023//
TI  - Towards a Perceptual Evaluation Framework for Lighting Estimation
N2  - rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms.
UR  - https://arxiv.org/abs/2312.04334
L1  - http://158.109.8.37/files/GDH2023.pdf
N1  - MACO; CIC
ID  - Justine Giroux2023
ER  -