ARaBIQA: A Novel Blind Image Quality Assessment Model for Augmented Reality
Aymen Sekhri, Mohamed–Chaker Larabi, Seyed Ali Amirshahi · 2025
Ensuring the quality of Augmented Reality (AR) experiences is crucial for achieving user satisfaction in many applications such as navigation, education, and healthcare. However, automatic AR quality assessment is challenging due to limited data and the lack of a reference image notion in real-world scenarios. Hence, blind quality assessment appears to be the only plausible solution. Existing blind IQA metrics often struggle to capture perceptual features in AR content as effectively as they do in natural images. We propose ARaBIQA, the first blind image quality assessment (BIQA) method designed specifically for AR content. Using a self-supervised approach, ARaBIQA learns low-level AR-specific features, including distortions and visual confusion, and combines them with high-level content features through a joint fine-tuning strategy to produce robust quality predictions. The experimental results show that ARaBIQA outperforms existing blind IQA metrics, and ablation studies further validate its effectiveness.