The Effect of Temporal Sub-sampling on the Accuracy of Volumetric Video Quality Assessment

Abstract

Volumetric video content has attracted increasing research interests over the last decade, as it facilitates the integration of dynamic real world content in virtual environments. Point cloud is one of the most common alternatives to represent volumetric video content. Yet, such representation requires an enormous data storage and pose significant greater pressures on compression algorithms compared to the standard 2D video. This challenge has unleashed a new wave in the development of novel point cloud compression technologies, which need to be evaluated in terms of production quality. Due to the high dimensionality of the data, evaluating the performances of relevant coding algorithms can be time consuming. This puts a barrier on optimizing coding algorithms with complex, but perceptually accurate, objective quality metrics. In this study, we thus explore the possibility of reducing temporal-dimension of the content under-evaluation, i.e., temporal sub-sampling, for objective quality evaluation without sacrificing from the correlation with the subjective opinion. In addition, we exploit different temporal pooling methods to further make the quality evaluation procedure more efficient. In total 30 different objective quality metrics were tested on the the V-SENSE volumetric video quality database. According to experimental results, there is no need to employ full frame-rate (30 fps) assessment to reach the meaningful correlation for the considered quality metrics. These observations could be referred to reduce the computation complexity regarding the evaluation and optimization of the relevant compression algorithms.