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

TL;DR

The paper introduces ARIQA-3DS, a large stereoscopic AR image quality dataset with real-world scenes and diverse degradations, enabling better assessment of AR visual quality and user experience.

Contribution

It presents the first large stereoscopic AR image quality dataset with real-world scenes, comprehensive subjective study data, and analysis of quality and discomfort factors.

Findings

Perceived quality is mainly affected by foreground degradations and transparency.

Discomfort symptoms increase gradually but remain manageable during viewing.

The dataset will be publicly released for benchmarking AR quality assessment models.

Abstract

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.