GLUQ: Global-Local Representation Learning for UHD Image Quality Assessment

Ultra-high-definition (UHD) blind image quality assessment (BIQA) is challenging because native-resolution inference is computationally expensive, whereas common strategies of resizing or patching may suppress scale-sensitive distortions and weaken the relationship between local artifacts and global scene context. To address this challenge, a Global-Local graph representation learning framework for UHD image Quality prediction (GLUQ) is proposed, which models structural dependencies among patches rather than treating them as independent views. Specifically, it samples aspect ratio-aligned patches from each UHD image, encodes these patches as graph nodes and constructs a hybrid k-nearest-neighbor graph via weighted spatial proximity and feature similarity. Residual graph convolution is used to propagate contextual information across regions, and gated attention pooling is used to aggregate patch-level evidence into image-level quality prediction. Besides, an exponential moving average normalized multi-objective loss function is adopted to stabilize the joint optimization of regression, correlation, and ranking objectives. Experiments on the UHD-IQA benchmark database show that GLUQ achieves the lowest RMSE among the compared methods with competitive PLCC and SRCC, indicating strong absolute-score calibration for UHD image quality prediction. The results suggest modeling global-local graph relations enhances quality prediction for UHD images with extremely high-resolution visual content.