ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision, for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video streaming distortions (e.g. video compression, rescaling, and transmission errors), and also 8 new distortion types related to AR/VR displays (e.g. light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications which require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification and design, visual comparison of results, and perceptually-guided quality optimization.

翻译：ColorVideoVDP是一种视频与图像质量度量指标，可对亮度和色彩在空间及时间维度上的视觉特性进行建模。该指标基于新型色度时空对比度敏感度与跨通道对比度掩蔽的心理物理学模型构建，并综合考虑了观看条件、显示设备的几何特性与光度特性。它经训练可预测常见视频流失真（如视频压缩、缩放及传输错误），以及8种与AR/VR显示相关的新型失真类型（如光源和波导非均匀性）。为应对后者，我们收集了包含336个失真视频的新型XR显示伪影视频质量数据集（XR-DAVID）。经XR-DAVID及文献中多个数据集的广泛测试表明，该指标相比现有度量方法在预测性能上有显著提升。ColorVideoVDP为众多需要联合自动评估亮度和色彩时空失真的新颖应用开辟了道路，包括视频流媒体、显示规范与设计、结果可视化比较以及基于感知引导的质量优化。