The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several efforts have been made towards this topic, but there {is a lack of} detailed survey in {the} literature. In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views. The current accessible datasets of DIBR-synthesized views are firstly reviewed{, followed} by a summary analysis of the representative state-of-the-art objective metrics. Then, the performances of different objective metrics are evaluated and discussed on all available datasets. Finally, we discuss the potential challenges and suggest possible directions for future research.
翻译：在3D视频应用程序中产生新观点的主要基本技术之一,如多视视频、自由视视频和虚拟现实(VR)。然而,对DIBR合成观点的质量评估与传统的2D图像/视频有很大不同。近年来,为这一专题作出了一些努力,但{在}文献中缺乏详细调查}。在本文件中,我们全面调查了目前对DIBR合成观点采取的各种做法。目前可获取的DIBR合成观点数据集首先经过审查,随后对具有代表性的2D图像/视频进行了简要分析。随后,对所有可用数据集的不同目标指标的绩效进行了评估和讨论。最后,我们讨论了潜在的挑战,并提出了未来研究的可能方向。
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