As Augmented Reality (AR) technologies advance towards immersive consumer adoption, the need for rigorous Quality of Experience (QoE) assessment becomes critical. However, existing datasets often lack ecological validity, relying on monocular viewing or simplified backgrounds that fail to capture the complex perceptual interplay, termed visual confusion, between real and virtual layers. To address this gap, we present ARIQA-3DS, the first large stereoscopic AR Image Quality Assessment dataset. Comprising 1,200 AR viewports, the dataset fuses high-resolution stereoscopic omnidirectional captures of real-world scenes with diverse augmented foregrounds under controlled transparency and degradation conditions. We conducted a comprehensive subjective study with 36 participants using a video see-through head-mounted display, collecting both quality ratings and simulator-sickness indicators. Our analysis reveals that perceived quality is primarily driven by foreground degradations and modulated by transparency levels, while oculomotor and disorientation symptoms show a progressive but manageable increase during viewing. ARIQA-3DS will be publicly released to serve as a comprehensive benchmark for developing next-generation AR quality assessment models.

翻译：随着增强现实（AR）技术迈向沉浸式消费应用普及，对体验质量（QoE）进行严格评估的需求变得至关重要。然而，现有数据集往往缺乏生态效度，依赖单目观看或简化背景，无法捕捉现实与虚拟层之间被称为"视觉混淆"的复杂感知交互。为解决这一缺口，我们提出了ARIQA-3DS——首个大规模立体AR图像质量评估数据集。该数据集包含1200个AR视口，融合了高分辨率立体全向真实场景采集与多样化增强前景，并在受控透明度与退化条件下构建。我们通过视频透视头戴显示器开展了包含36名参与者的全面主观研究，收集了质量评分与模拟器晕动症指标。分析表明，感知质量主要受前景退化驱动，并受透明度水平调节，而眼球运动与定向障碍症状在观看过程中呈现渐进但可控的增长。ARIQA-3DS将公开发布，作为开发下一代AR质量评估模型的综合性基准。