首个立体增强现实图像质量数据集，助力真实体验评估
ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality
- 构建1200张立体AR画面，融合真实场景与虚拟前景
- 发现前景失真主导感知质量，透明度影响感知效果
- 适合研究沉浸式体验与视觉疲劳的开发者和学者
随着增强现实（AR）技术向沉浸式消费应用发展，用户体验质量（QoE）评估变得至关重要。然而，现有数据集往往缺乏生态有效性，依赖单目观察或简化背景，无法捕捉真实与虚拟图层间复杂的感知干扰现象——视觉混淆。为此，我们提出ARIQA-3DS，首个大规模立体增强现实图像质量评估数据集。该数据集包含1,200个AR视口，将高分辨率立体全景真实场景与多种增强前景在受控透明度和退化条件下融合。我们通过视频透视头戴设备对36名参与者进行了全面主观测试，收集了质量评分与晕动症指标。分析表明，感知质量主要由前景退化决定，并受透明度调节；而眼动与方向感不适症状随观看时间呈渐进上升但可控。ARIQA-3DS将公开发布，作为下一代AR质量评估模型的综合性基准。
原文摘要 · Abstract (English)
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.
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