Ultra-High-Definition Image Quality Assessment via Graph Representation Learning

TL;DR

This paper introduces UHD-GCN-BIQA, a graph-based framework for ultrahigh-definition image quality assessment that models structural dependencies among image regions to improve prediction accuracy.

Contribution

It proposes a novel graph representation learning approach that explicitly captures relationships among sampled image regions for better UHD image quality assessment.

Findings

Achieves PLCC = 0.7784 and SRCC = 0.8019 on UHD-IQA benchmark.

Obtains the lowest RMSE of 0.0519 among compared methods.

Demonstrates the effectiveness of graph-based region relation modeling for UHD image quality prediction.

Abstract

Blind image quality assessment (BIQA) for ultrahighdefinition (UHD) images remains challenging because native-resolution inference is computationally expensive, whereas aggressive resizing or isolated cropping may suppress scale-sensitive distortions and weaken the relationship between local artifacts and global scene context. This paper aims to improve UHD-BIQA by explicitly modeling the structural dependencies among sampled image regions rather than treating them as independent views, and a graph representation learning framework UHD-GCN-BIQA is proposed. The framework samples aspect-ratio-aligned patches from each UHD image, encodes them as graph nodes, and constructs a hybrid k-nearest-neighbor graph using spatial proximity and feature similarity. Residual graph convolution is used to propagate contextual information across regions, and gated attention pooling aggregates patchlevel evidence into an imagelevel quality prediction. An exponential moving average normalized multiobjective loss function is adopted to stabilize the joint optimization of regression, correlation, and ranking objectives. Experiments on the UHD-IQA benchmark show that UHD-GCN-BIQA achieves PLCC = 0.7784, SRCC = 0.8019, and RMSE = 0.0519, obtaining competitive correlation performance and the lowest RMSE among the compared methods. These results indicate that graph-based region relation modeling is effective for UHD image quality assessment, particularly for improving absolute quality score estimation under high-resolution visual content.