High Dynamic Range (HDR) videos have enjoyed a surge in popularity in recent
years due to their ability to represent a wider range of contrast and color
than Standard Dynamic Range (SDR) videos. Although HDR video capture has seen
increasing popularity because of recent flagship mobile phones such as Apple
iPhones, Google Pixels, and Samsung Galaxy phones, a broad swath of consumers
still utilize legacy SDR displays that are unable to display HDR videos. As
result, HDR videos must be processed, i.e., tone-mapped, before streaming to a
large section of SDR-capable video consumers. However, server-side tone-mapping
involves automating decisions regarding the choices of tone-mapping operators
(TMOs) and their parameters to yield high-fidelity outputs. Moreover, these
choices must be balanced against the effects of lossy compression, which is
ubiquitous in streaming scenarios. In this work, we develop a novel, efficient
model of objective video quality named Cut-FUNQUE that is able to accurately
predict the visual quality of tone-mapped and compressed HDR videos. Finally,
we evaluate Cut-FUNQUE on a large-scale crowdsourced database of such videos
and show that it achieves state-of-the-art accuracy.

开发了一种名为 Cut-FUNQUE 的新颖高效的客观视频质量模型，能够准确预测经色调映射和压缩的 HDR 视频的视觉质量，并在大规模众包数据库上展示它达到了最先进的准确性。

Cut-FUNQUE: 壓縮調色高動態範圍視頻的客觀質量模型

Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High  Dynamic Range Videos

High Dynamic Range (HDR) videos are able to represent wider ranges of
contrasts and colors than Standard Dynamic Range (SDR) videos, giving more
vivid experiences. Due to this, HDR videos are expected to grow into the
dominant video modality of the future. However, HDR videos are incompatible
with existing SDR displays, which form the majority of affordable consumer
displays on the market. Because of this, HDR videos must be processed by
tone-mapping them to reduced bit-depths to service a broad swath of SDR-limited
video consumers. Here, we analyze the impact of tone-mapping operators on the
visual quality of streaming HDR videos. To this end, we built the first
large-scale subjectively annotated open-source database of compressed
tone-mapped HDR videos, containing 15,000 tone-mapped sequences derived from 40
unique HDR source contents. The videos in the database were labeled with more
than 750,000 subjective quality annotations, collected from more than 1,600
unique human observers. We demonstrate the usefulness of the new subjective
database by benchmarking objective models of visual quality on it. We envision
that the new LIVE Tone-Mapped HDR (LIVE-TMHDR) database will enable significant
progress on HDR video tone mapping and quality assessment in the future. To
this end, we make the database freely available to the community at
this https URL

对现有显示器不兼容的 HDR 视频进行色调映射处理以服务于 SDR 受限的用户，通过分析合成的 15,000 个经过主观质量注释的 HDR 视频数据库对色调映射算法的视觉质量进行评估和基准测试，为 HDR 视频的色调映射和质量评估领域带来显著进展。

压缩调制高动态范围视频的主观质量评价

Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic  Range Videos

We introduce HIDRO-VQA, a no-reference (NR) video quality assessment model
designed to provide precise quality evaluations of High Dynamic Range (HDR)
videos. HDR videos exhibit a broader spectrum of luminance, detail, and color
than Standard Dynamic Range (SDR) videos. As HDR content becomes increasingly
popular, there is a growing demand for video quality assessment (VQA)
algorithms that effectively address distortions unique to HDR content. To
address this challenge, we propose a self-supervised contrastive fine-tuning
approach to transfer quality-aware features from the SDR to the HDR domain,
utilizing unlabeled HDR videos. Our findings demonstrate that self-supervised
pre-trained neural networks on SDR content can be further fine-tuned in a
self-supervised setting using limited unlabeled HDR videos to achieve
state-of-the-art performance on the only publicly available VQA database for
HDR content, the LIVE-HDR VQA database. Moreover, our algorithm can be extended
to the Full Reference VQA setting, also achieving state-of-the-art performance.
Our code is available publicly at this https URL

通过自监督对比微调方法，将有标记的标准动态范围（SDR）视频的质量感知特征转移到无标记高动态范围（HDR）视频领域，从而实现对 HDR 视频的精确质量评估，并取得了最先进的性能。