  Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively
assess the human perceptual quality of omnidirectional images (ODIs) without
relying on pristine-quality image information. It is becoming more significant
with the increasing advancement of virtual reality (VR) technology. However,
the quality assessment of ODIs is severely hampered by the fact that the
existing BOIQA pipeline lacks the modeling of the observer's browsing process.
To tackle this issue, we propose a novel multi-sequence network for BOIQA
called Assessor360, which is derived from the realistic multi-assessor ODI
quality assessment procedure. Specifically, we propose a generalized Recursive
Probability Sampling (RPS) method for the BOIQA task, combining content and
details information to generate multiple pseudo-viewport sequences from a given
starting point. Additionally, we design a Multi-scale Feature Aggregation (MFA)
module with a Distortion-aware Block (DAB) to fuse distorted and semantic
features of each viewport. We also devise Temporal Modeling Module (TMM) to
learn the viewport transition in the temporal domain. Extensive experimental
results demonstrate that Assessor360 outperforms state-of-the-art methods on
multiple OIQA datasets. The code and models are available at
https://github.com/TianheWu/Assessor360.
