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方法，它同时对亮度和颜色的空间与时间视觉特性进行建模。该度量建立在色度时空对比敏感度与跨通道对比掩蔽的新型心理物理学模型之上，并考虑了显示设备的观看条件、几何特性与光度特性。该方法经训练可用于预测常见的视频流失真（例如视频压缩、重缩放与传输错误），以及八种与 AR/VR 显示相关的新型失真类型（例如光源与波导不均匀性）。针对后一类应用，我们收集并构建了新颖的 XR 显示伪影视频质量数据集（XR-DAVID），包含 336 段失真视频。在 XR-DAVID 以及多个文献数据集上的广泛测试表明，与现有度量方法相比，本方法在预测性能上取得了显著提升。ColorVideoVDP 为许多需要联合自动评估亮度与颜色失真时空特性的新应用开启了大门，包括视频流传输、显示设备规格与设计、结果可视化比较以及感知引导的质量优化。