TransformAR: A light-weight transformer-based metric for Augmented Reality quality assessment

As Augmented Reality (AR) technology continues to gain traction in various sectors, ensuring a superior user experience has become an essential challenge for both academic researchers and industry professionals. However, the task of automatically predicting the quality of AR images remains difficult due to several inherent challenges, particularly the issue of visual confusion arising from the overlap of virtual and real-world elements. This paper introduces transformAR, a novel and efficient transformer-based framework designed to objectively assess the quality of AR images. The proposed model uses pre-trained vision transformers to capture content features from AR images, calculates distance vectors to measure the impact of distortions, and employs cross-attention-based decoders to effectively model the perceptual qualities of the AR images. Additionally, the training framework uses regularization techniques and label smoothing-like method to reduce the risk of overfitting. Through comprehensive experiments, we demonstrate that transformAR outperforms existing state-of-the-art approaches, offering a more reliable and scalable solution for AR image quality assessment. • Introduces a transformer-based AR-IQA metric for augmented reality image quality assessment. • Designs a lightweight encoder–decoder transformer for efficient global quality feature extraction. • Adapts a label smoothing strategy to reduce overconfident predictions and improve robustness. • Models perceptual confusion by jointly analyzing real background, virtual foreground, and AR images. • Performs extensive benchmarking on ARIQA and ARIQA-PICO datasets to validate generalization and robustness.