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 detailed information to generate multiple pseudo viewport sequences from a given starting point. Additionally, we design a Multi-scale Feature Aggregation (MFA) module with Distortion-aware Block (DAB) to fuse distorted and semantic features of each viewport. We also devise 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.

翻译：摘要：盲全景图像质量评估（BOIQA）旨在不依赖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）技术的不断发展，其重要性日益凸显。然而，现有BOIQA流程缺乏对观察者浏览过程建模，严重制约了全景图像的质量评估。为解决这一问题，我们提出了一种新颖的BOIQA多序列网络Assessor360，该网络源于真实的多评估者ODI质量评估流程。具体而言，我们为BOIQA任务提出了一种广义递归概率采样（RPS）方法，通过结合内容与细节信息，从给定起始点生成多个伪视口序列。此外，我们设计了带失真感知模块（DAB）的多尺度特征聚合（MFA）模块，以融合每个视口的失真与语义特征。我们还设计了TMM来学习视口在时间域中的转换。大量实验结果表明，Assessor360在多个OIQA数据集上均优于现有最优方法。