VOLVQAD: An MPEG V-PCC Volumetric Video Quality Assessment Dataset

Abstract

We present VOLVQAD, a volumetric video quality assessment dataset consisting 7,680 ratings on 376 video sequences from 120 participants. The volumetric video sequences are first encoded with MPEG V-PCC using 4 different avatar models and 16 quality variations, and then rendered into test videos for quality assessment using 2 different background colors and 16 different quality switching patterns. The dataset is useful for researchers who wish to understand the impact of volumetric video compression on subjective quality. Analysis of the collected data are also presented in this paper.