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：盲全向图像质量评估（BOIQA）旨在不依赖原始质量图像信息的情况下，客观评估全向图像（ODI）的人类感知质量。随着虚拟现实（VR）技术的不断发展，这一任务日益重要。然而，现有BOIQA流程因缺乏对观察者浏览过程的建模，严重制约了ODI质量评估的效果。为解决该问题，我们提出一种新颖的BOIQA多序列网络——Assessor360，该网络源于现实中的多评估者ODI质量评估流程。具体而言，我们针对BOIQA任务提出一种广义递归概率采样（RPS）方法，通过融合内容与细节信息，从给定起点生成多个伪视口序列。此外，我们设计了一种带失真感知模块（DAB）的多尺度特征聚合（MFA）模块，用于融合每个视口的失真特征与语义特征，并设计了TMM以学习时域中的视口切换。大量实验结果表明，Assessor360在多个OIQA数据集上均优于现有最先进方法。