Algorithm evaluation study demonstrates superior video quality assessment in depth-image-based synthesized videos, indicating improved benchmarking for multi-view video synthesis.
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.
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Huang et al. (2020) studied this question.
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