ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality
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 degra...
| Authors | Aymen Sekhri, Seyed Ali Amirshahi, Mohamed-Chaker Larabi |
|---|---|
| Primary category | eess.IV |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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