Progress 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.

通过进行一系列实验，我们发现现有的图像质量评估指标并不能正确地代表人们对光照估计方法的偏好，然而通过综合学习多个指标，我们可以更准确地表示人类的首选项，从而为评估未来的光照估计算法提供了新的感知框架。