ARIQA-3DS: A Stereoscopic Image Quality Assessment Dataset for Realistic Augmented Reality

As AR technologies advance towards immersive consumer adoption, the need for rigorous quality-of-experience assessment becomes critical. Existing datasets often lack ecological validity, relying on monocular viewing or simplified backgrounds that fail to capture visual confusion — the complex perceptual interplay between real and virtual layers.

ARIQA-3DS is the first large stereoscopic AR image quality assessment dataset. Its 1,200 AR viewports fuse high-resolution stereoscopic omnidirectional captures of real-world scenes with diverse augmented foregrounds under controlled transparency and degradation conditions. A subjective study with 36 participants using a video see-through head-mounted display collected both quality ratings and simulator-sickness indicators.

Our analysis shows that perceived quality is primarily driven by foreground degradations and modulated by transparency levels, while oculomotor and disorientation symptoms increase progressively but manageably during viewing. The dataset is released publicly as a benchmark for next-generation AR quality models.