Single-image high dynamic range (SI-HDR) reconstruction has recently emerged
as a problem well-suited for deep learning methods. Each successive technique
demonstrates an improvement over existing methods by reporting higher image
quality scores. This paper, however, highlights that such improvements in
objective metrics do not necessarily translate to visually superior images. The
first problem is the use of disparate evaluation conditions in terms of data
and metric parameters, calling for a standardized protocol to make it possible
to compare between papers. The second problem, which forms the main focus of
this paper, is the inherent difficulty in evaluating SI-HDR reconstructions
since certain aspects of the reconstruction problem dominate objective
differences, thereby introducing a bias. Here, we reproduce a typical
evaluation using existing as well as simulated SI-HDR methods to demonstrate
how different aspects of the problem affect objective quality metrics.
Surprisingly, we found that methods that do not even reconstruct HDR
information can compete with state-of-the-art deep learning methods. We show
how such results are not representative of the perceived quality and that
SI-HDR reconstruction needs better evaluation protocols.

本文旨在探讨 SI-HDR 重建评估中存在的问题，并提出了统一标准以进行有效的比较，同时指出深度学习方法的提高并不一定能够反映出视觉上的优越性，呼吁对 SI-HDR 重建进行更好的评估协议。