Towards Light-Weight Transformer-Based Quality Assessment Metric for Augmented Reality

With the rise of Augmented Reality (AR) technology, which enhances the real world by overlaying computer-generated content, immersive experiences are being offered in education, entertainment, healthcare, … Assessing the quality of AR scenarios is crucial for understanding and improving user satisfaction and engagement. However, developing objective AR quality assessment methods is challenging due to the lack of data and the inherent complexity of technology, particularly in the presence of visual confusion. Existing convolution neural network-based approaches suffer from limited receptive fields and are not effective at capturing global information in visually confused AR scenarios. Additionally, to the best of our knowledge, exploring transformer capabilities for AR quality assessment is missing. Therefore, this study introduces transformAR, a lightweight transformer-based model for objective quality assessment in AR applications. This approach leverages pretrained vision transformer-based encoders to capture image content information, computes distance vectors to quantify distortions, and employs cross-attention-based decoders to model perceptual quality features. The model also integrates adapted regularization techniques and label smoothing to mitigate overfitting. Experimental results demonstrate the effectiveness of transformAR, outperforming the few existing state-of-the-art methods.