A Benchmark of DIBR Synthesized View Quality Assessment Metrics on a New Database for Immersive Media Applications

Abstract

Depth-image-based rendering (DIBR) is a funda-mental technology in several 3-D-related applications, such as free viewpoint video, virtual reality, and augmented reality. However, new challenges have also been brought in assessing the quality of DIBR-synthesized views since this process induces some new types of distortions, which are inherently different from the distortion caused by video coding. In this paper, we present a new DIBR-synthesized image database with the associated subjective scores. We also test the performances of the state-of-the-art objective quality metrics on this database. This paper focuses on the distortions only induced by different DIBR synthesis methods. Seven state-of-the-art DIBR algorithms, including inter-view synthesis and single-view-based synthesis methods, are considered in this database. The quality of synthesized views was assessed subjectively by 41 observers and objectively using 14 state-of-the-art objective metrics. Subjective test results show that the interview synthesis methods, having more input information, significantly outperform the single-view-based ones. Correlation results between the tested objective metrics and the subjective scores on this database reveal that further studies are still needed for a better objective quality metric dedicated to the DIBR-synthesized views.