  Omnidirectional videos (ODVs) play an increasingly important role in the
application fields of medical, education, advertising, tourism, etc. Assessing
the quality of ODVs is significant for service-providers to improve the user's
Quality of Experience (QoE). However, most existing quality assessment studies
for ODVs only focus on the visual distortions of videos, while ignoring that
the overall QoE also depends on the accompanying audio signals. In this paper,
we first establish a large-scale audio-visual quality assessment dataset for
omnidirectional videos, which includes 375 distorted omnidirectional
audio-visual (A/V) sequences generated from 15 high-quality pristine
omnidirectional A/V contents, and the corresponding perceptual audio-visual
quality scores. Then, we design three baseline methods for full-reference
omnidirectional audio-visual quality assessment (OAVQA), which combine existing
state-of-the-art single-mode audio and video QA models via multimodal fusion
strategies. We validate the effectiveness of the A/V multimodal fusion method
for OAVQA on our dataset, which provides a new benchmark for omnidirectional
QoE evaluation. Our dataset is available at https://github.com/iamazxl/OAVQA.
