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.

翻译：盲全景图像质量评估（BOIQA）旨在无需原始质量图像信息的情况下，客观评估全景图像（ODI）的人类感知质量。随着虚拟现实（VR）技术的日益发展，这一方法变得愈发重要。然而，现有BOIQA流程缺乏对观察者浏览过程的建模，严重制约了ODI的质量评估。为解决该问题，我们从真实的多评估者全景图像质量评估流程出发，提出一种名为Assessor360的BOIQA多序列网络。具体而言，我们提出一种用于BOIQA任务的广义递归概率采样（RPS）方法，通过结合内容与细节信息，从给定起点生成多个伪视口序列。此外，我们设计了带失真感知块（DAB）的多尺度特征聚合（MFA）模块，以融合每个视口的失真特征与语义特征；同时，提出时间建模模块（TMM）用于学习视口在时间域上的过渡。大量实验结果表明，在多个OIQA数据集上，Assessor360的性能超越了现有最先进方法。代码与模型已发布于https://github.com/TianheWu/Assessor360。