Cut-FUNQUE：一种用于压缩色调映射高动态范围视频的客观质量模型
Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos
AI总结：
针对HDR视频在服务器端色调映射与有损压缩下的质量评估问题，提出名为Cut-FUNQUE的新型客观视频质量模型，能准确预测压缩色调映射HDR视频的视觉质量并在大规模众包数据库上达到最先进准确度。
摘要：
近年来，高动态范围（HDR）视频因其能够比标准动态范围（SDR）视频呈现更宽的对比度和色彩范围而迅速普及。尽管由于 Apple iPhones、Google Pixels 和 Samsung Galaxy phones 等近期旗舰手机的推出，HDR 视频拍摄日益普及，但大量消费者仍在使用无法显示 HDR 视频的传统 SDR 显示器。因此，在向大量支持 SDR 的视频消费者流式传输之前，必须对 HDR 视频进行处理，即进行色调映射。然而，服务器端色调映射涉及自动化决策，即选择色调映射算子（TMOs）及其参数以产生高保真输出。此外，这些选择必须与有损压缩的影响相平衡，而有损压缩在流媒体场景中无处不在。在这项工作中，我们开发了一种名为 Cut-FUNQUE 的新型高效客观视频质量模型，能够准确预测色调映射和压缩 HDR 视频的视觉质量。最后，我们在此类视频的大规模众包数据库上评估了 Cut-FUNQUE，并表明它实现了最先进的准确度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
