  High Dynamic Range (HDR) content creation has become an important topic for
modern media and entertainment sectors, gaming and Augmented/Virtual Reality
industries. Many methods have been proposed to recreate the HDR counterparts of
input Low Dynamic Range (LDR) images/videos given a single exposure or
multi-exposure LDRs. The state-of-the-art methods focus primarily on the
preservation of the reconstruction's structural similarity and the pixel-wise
accuracy. However, these conventional approaches do not emphasize preserving
the artistic intent of the images in terms of human visual perception, which is
an essential element in media, entertainment and gaming. In this paper, we
attempt to study and fill this gap. We propose an architecture called
ArtHDR-Net based on a Convolutional Neural Network that uses multi-exposed LDR
features as input. Experimental results show that ArtHDR-Net can achieve
state-of-the-art performance in terms of the HDR-VDP-2 score (i.e., mean
opinion score index) while reaching competitive performance in terms of PSNR
and SSIM.
