  Capturing high dynamic range (HDR) images (videos) is attractive because it
can reveal the details in both dark and bright regions. Since the mainstream
screens only support low dynamic range (LDR) content, tone mapping algorithm is
required to compress the dynamic range of HDR images (videos). Although image
tone mapping has been widely explored, video tone mapping is lagging behind,
especially for the deep-learning-based methods, due to the lack of HDR-LDR
video pairs. In this work, we propose a unified framework (IVTMNet) for
unsupervised image and video tone mapping. To improve unsupervised training, we
propose domain and instance based contrastive learning loss. Instead of using a
universal feature extractor, such as VGG to extract the features for similarity
measurement, we propose a novel latent code, which is an aggregation of the
brightness and contrast of extracted features, to measure the similarity of
different pairs. We totally construct two negative pairs and three positive
pairs to constrain the latent codes of tone mapped results. For the network
structure, we propose a spatial-feature-enhanced (SFE) module to enable
information exchange and transformation of nonlocal regions. For video tone
mapping, we propose a temporal-feature-replaced (TFR) module to efficiently
utilize the temporal correlation and improve the temporal consistency of video
tone-mapped results. We construct a large-scale unpaired HDR-LDR video dataset
to facilitate the unsupervised training process for video tone mapping.
Experimental results demonstrate that our method outperforms state-of-the-art
image and video tone mapping methods. Our code and dataset are available at
https://github.com/cao-cong/UnCLTMO.
