Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video
This addresses the need for quality assessment tools in the Metaverse for UGC-ODVs, but it is incremental as it builds on existing AVQA methods by applying them to a new dataset.
The researchers tackled the lack of audio-visual quality assessment (AVQA) for user-generated omnidirectional videos (UGC-ODVs) by constructing a dataset of 300 videos captured with omnidirectional cameras and developing a baseline model, which achieved optimal performance on this dataset.
In response to the rising prominence of the Metaverse, omnidirectional videos (ODVs) have garnered notable interest, gradually shifting from professional-generated content (PGC) to user-generated content (UGC). However, the study of audio-visual quality assessment (AVQA) within ODVs remains limited. To address this, we construct a dataset of UGC omnidirectional audio and video (A/V) content. The videos are captured by five individuals using two different types of omnidirectional cameras, shooting 300 videos covering 10 different scene types. A subjective AVQA experiment is conducted on the dataset to obtain the Mean Opinion Scores (MOSs) of the A/V sequences. After that, to facilitate the development of UGC-ODV AVQA fields, we construct an effective AVQA baseline model on the proposed dataset, of which the baseline model consists of video feature extraction module, audio feature extraction and audio-visual fusion module. The experimental results demonstrate that our model achieves optimal performance on the proposed dataset.