一个数据集和模型用于反向调色映射的HDR视频的视觉质量评估
概括
这项研究引入了一个新的视觉质量评估模型,用于反向调色映射的HDR视频,使用SDR视频作为参考. 该模型有效地评估了ITM算法性能,并解决了HDR视频质量评估中的关键差距.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人与计算机的交互
背景情况:
- 反向音调映射 (ITM) 增强标准动态范围 (SDR) 视频,用于高动态范围 (HDR) 显示器.
- 客观的视觉质量评估 (VQA) 对于评估ITM算法至关重要,但ITM-HDR-视频的专用模型缺乏.
研究的目的:
- 为满足ITM-HDR-视频的专用VQA模型的需求.
- 引入一个新的SDR参考HDR (SD-R-HD) VQA模型和ITM-HDR-视频质量评估的第一个公共数据集.
主要方法:
- 开发了一个SD-R-HD VQA模型,使用SDR视频作为参考.
- 提取了ITM操作的特征 (全球映射,本地补偿).
- 由ITM引入的模拟跨框架不一致性.
主要成果:
- 创建了ITM-HDR-VQA数据集,包含200个视频和心理视觉实验中的平均意见分数.
- 拟议的SD-R-HD VQA模型在实验中显著优于现有的最先进的VQA模型.
结论:
- SD-R-HD VQA模型为ITM-HDR-视频提供了有效的质量评估.
- 引入的数据集和模型填补了HDR视频质量评估中的重大差距.
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