Depth-image-based rendering (DIBR), as the most popular view synthesis method, is commonly used in the application of multi-view and free-viewpoint videos. However, the quality evaluation of DIBR-synthesised videos remains largely unexplored, which may hinder the development of more advanced view synthesis technology. With this motivation, this Letter presents a new quality metric for DIBR-synthesised videos. Specifically, the disoccluded regions are first detected based on an adaptive threshold to quantify geometric distortions. An energy-based sequence mapping strategy is proposed to portray spatiotemporal inconsistency by calculating first-order and second-order similarities in the gradient magnitude domain and the Laplace-of-Gaussian domain, respectively. Finally, the overall quality score is generated by pooling the scores of geometric distortion and spatiotemporal inconsistency. Experimental results demonstrate that the proposed metric outperforms the state-of-the-art metrics dedicated to DIBR-synthesised images and videos.

中文翻译：

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通过测量几何失真和时空不一致来进行DIBR合成的视频质量评估

基于深度图像的渲染（DIBR）是最流行的视图合成方法，通常用于多视点和自由视点视频的应用中。但是，DIBR合成视频的质量评估在很大程度上尚待探索，这可能会阻碍更高级的视图合成技术的发展。出于这种动机，这封信为DIBR合成的视频提出了一种新的质量指标。具体地，首先基于自适应阈值检测被遮挡的区域以量化几何失真。提出了一种基于能量的序列映射策略，通过计算梯度幅度域和高斯拉普拉斯域中的一阶和二阶相似性来刻画时空不一致。最后，通过合并几何变形和时空不一致的分数来生成整体质量分数。实验结果表明，所提出的指标优于专门用于DIBR合成的图像和视频的最新指标。