No-Reference Multi-Level Video Quality Assessment Metric for 3D-Synthesized Videos

Abstract

The visual quality of 3D-synthesized videos is closely related to the development and broadcasting of immersive media such as free-viewpoint videos and six degrees of freedom navigation. Therefore, studying the 3D-Synthesized video quality assessment is helpful to promote the popularity of immersive media applications. Inspired by the texture compression, depth compression and virtual view synthesis polluting the visual quality of 3D-synthesized videos at pixel-, structure- and content-levels, this paper proposes a Multi-Level 3D-Synthesized Video Quality Assessment algorithm, namely ML-SVQA, which consists of a quality feature perception module and a quality feature regression module. Specifically, the quality feature perception module firstly extracts motion vector fields of the 3D-synthesized video at pixel-, structure- and content-levels by combining the perception mechanism of human visual system. Then, the quality feature perception module measures the temporal flicker distortion intensity in the no-reference environment by calculating the self-similarity of adjacent motion vector fields. Finally, the quality feature regression module uses the machine learning algorithm to learn the mapping of the developed quality features to the quality score. Experiments constructed on the public IRCCyN/IVC and SIAT synthesized video datasets show that our ML-SVQA is more effective than state-of-the-art image/video quality assessment methods in evaluating the quality of 3D-Synthesized videos.