  Due to the strong correlation between visual attention and perceptual
quality, many methods attempt to use human saliency information for image
quality assessment. Although this mechanism can get good performance, the
networks require human saliency labels, which is not easily accessible for
omnidirectional images (ODI). To alleviate this issue, we propose a spatial
attention-based perceptual quality prediction network for non-reference quality
assessment on ODIs (SAP-net). To drive our SAP-net, we establish a large-scale
IQA dataset of ODIs (IQA-ODI), which is composed of subjective scores of 200
subjects on 1,080 ODIs. In IQA-ODI, there are 120 high quality ODIs as
reference, and 960 ODIs with impairments in both JPEG compression and map
projection. Without any human saliency labels, our network can adaptively
estimate human perceptual quality on impaired ODIs through a self-attention
manner, which significantly promotes the prediction performance of quality
scores. Moreover, our method greatly reduces the computational complexity in
quality assessment task on ODIs. Extensive experiments validate that our
network outperforms 9 state-of-the-art methods for quality assessment on ODIs.
The dataset and code have been available on \url{
https://github.com/yanglixiaoshen/SAP-Net}.
