ARIQA-3DS：一种用于现实增强现实的立体图像质量评估数据集
ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality
AI总结：
本文提出ARIQA-3DS数据集，用于评估增强现实中的图像质量，通过融合高分辨率立体全景拍摄与多样化的增强前景，揭示前景退化和透明度对感知质量的影响，为下一代AR质量评估模型提供基准。
摘要：
随着增强现实（AR）技术向沉浸式消费应用迈进，对质量体验（QoE）的严格评估变得至关重要。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，无法捕捉真实与虚拟层之间复杂的视觉混淆现象。为此，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1,200个AR视口，融合了高分辨率立体全景拍摄的真实世界场景与多样化的增强前景，在受控透明度和退化条件下进行。我们通过36名参与者使用视频透视头戴显示器进行了全面的主观研究，收集了质量和模拟不适指标。我们的分析表明，感知质量主要由前景退化驱动，并受透明度水平调节，而眼动和定向症状在观看过程中呈渐进但可控的增加趋势。ARIQA-3DS将公开发布，作为开发下一代AR质量评估模型的全面基准。
英文摘要：
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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