Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant challenges for panoramic video quality assessment (PVQA). In this work, we propose an end-to-end optimized, blind PVQA method with explicit modeling of user viewing patterns through visual scanpaths. Our method consists of two modules: a scanpath generator and a quality assessor. The scanpath generator is initially trained to predict future scanpaths by minimizing their expected code length and then jointly optimized with the quality assessor for quality prediction. Our blind PVQA method enables direct quality assessment of panoramic images by treating them as videos composed of identical frames. Experiments on three public panoramic image and video quality datasets, encompassing both synthetic and authentic distortions, validate the superiority of our blind PVQA model over existing methods.

翻译：全景视频具有提供沉浸式和交互式观看体验的优势。然而，其球面特性导致用户观看行为多样且不确定，这给全景视频质量评估带来了重大挑战。本文提出了一种端到端优化的盲全景视频质量评估方法，通过视觉扫描路径显式建模用户观看模式。该方法包含两个模块：扫描路径生成器和质量评估器。扫描路径生成器首先通过最小化期望编码长度来训练预测未来的扫描路径，随后与质量评估器联合优化以实现质量预测。我们的盲全景视频质量评估方法通过将全景图像视为由相同帧组成的视频，能够直接评估其质量。在三个涵盖合成失真和真实失真的公开全景图像与视频质量数据集上的实验，验证了所提出的盲全景视频质量评估模型相较于现有方法的优越性。