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

CIF：中：了解 3D 视频质量及其在 3D 视频处理和通信中的应用

• 批准号: 1065305
• 负责人: Truong Nguyen
• 金额: $ 96.02万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-15 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065305&HistoricalAwards=false
• 关键词: CIF; Medium; Understanding; Quality; 3D

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.

根据人类视觉系统中立体处理的一般原理对3D图像/视频感知的研究正被用于为3D视频应用导出3D质量度量，以提供最佳的3D体验。此外，虽然视觉系统可以表示由高空间频率分量携带的2D图像的精细细节（即使图像快速变化），但它不能以空间或时间的超高分辨率跟踪深度变化。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，在视网膜水平上再现深度的准确性的一些偏差在感知上是显著的，而其他偏差则不那么显著。在深度信号的空间和时间轮廓的范围内感知图像保真度的测量被用于指导最佳视频处理技术的发展，并且允许评估诸如多视图对视频+深度的替代3D视频编码算法的优点和限制）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。从这些结果中，质量度量标准被开发并集成到视频处理和通信应用中。以人为中心的视差估计和视图合成算法正在开发用于视频处理和通信应用;这也可以用于提高目标检测，分类和跟踪的性能，并生成用于自动立体显示的多视图，其在3D启用的诊断医学成像和手术系统中找到应用。