CIF: Medium: Understanding Quality of 3D Video with Applications in 3D Video Processing and Communications

根据人类视觉系统立体处理的一般原理，对3D图像/视频感知的研究被用于推导3D视频应用的3D质量度量，以提供最佳的3D体验。人类观察者可以高灵敏度地检测深度差异，但精度有限。此外，虽然视觉系统可以表示由高空间频率分量携带的2D图像的细节(即使图像变化很快)，但它不能以相对较高的空间或时间分辨率跟踪深度的变化。因此，立体深度的表示在带宽和比特深度上都受到限制。由于这些限制，在视网膜水平上呈现深度的准确性的一些偏差在视觉上是显著的，而另一些则不是很显著。对深度信号的一系列空间和时间轮廓上的感知图像保真度的测量被用于指导最佳视频处理技术的开发，并允许评估可选3D视频编码算法的优点和局限性(例如多视角与视频+深度)。视觉实验研究使用各种编码方案生成的3D视频中的感知保真度和感知图像质量。根据这些结果，制定了质量指标，并将其整合到视频处理和通信应用程序中。一种以人为中心的视差估计和视点合成算法正在开发中，用于视频处理和通信应用；这也可以用来提高目标检测、分类和跟踪的性能，并为自动立体显示生成多个视点，这在3D使能的诊断医学成像和外科系统中得到了应用。

Research on 3D image/video perception in the light of general principles of stereoprocessing in the human visual system is being used to derive 3D quality metrics for 3Dvideo applications in order to deliver the best 3D experience.Human observers can detect differences in depth with high sensitivity, but limitedprecision. Moreover, while the visual system can represent fine details of the 2D imagethat are carried by high spatial frequency components (even when the image is rapidlychanging), it can not track variations in depth with comparably high resolution in spaceor time. Thus the representation of stereoscopic depth is restricted both in bandwidth andin bit depth. Because of those limitations, some deviations from accuracy in therepresentation of depth at the retinal level are perceptually salient, and others less so.Measurements of perceived image fidelity across a range of spatial and temporal profilesfor the depth signal are being used to guide the development of optimal video processingtechniques, and to allow evaluation of the advantages and limitations of alternative 3Dvideo coding algorithms such as multiview versus video+depth).Vision experiments investigate both perceived fidelity and perceived image quality in 3Dvideo generated using a variety of encoding schemes. From those results quality metricsare developed and integrated into video processing and communications applications. Ahuman-centric disparity estimation and view synthesis algorithm is being developed forvideo processing and communications applications; this can also be used to improve theperformance of object detection, classification and tracking, and to generate multi viewsfor autostereoscopic display, which finds applications in 3D enabled diagnostic medicalimaging and surgical systems.