Graph-Perceptron with Semantic Fidelity for No-Reference Super-Resolution Image Quality Assessment

Super-resolution (SR) images, generated by advanced algorithms to enhance resolution under hardware constraints, are increasingly applied across various multimedia tasks. However, the absence of paired high-resolution (HR) reference image and the inherent ill-posedness of SR reconstruction present key challenges for SR image quality assessment (SR-IQA). Full-reference methods become inapplicable, while the reduced-reference methods relying on one low-resolution (LR) image offer limited reliability. To address these issues, I propose the SQer, a no-reference SR-IQA method based on a graph perceptron with semantic fidelity. The SQer first extracts perceptual and hierarchical SR image features using a superposition nonlinear feature pooling. These features are transformed into graph vector representations, allowing semantic information learning via a graph-structured attention perceptron. Finally, the resulting graphs are globally average-pooled into a semantic embedding, which is then processed by a multilayer perceptron to predict the SR image quality score. Extensive experiments on multiple SR-IQA benchmarks demonstrate that my proposed SQer significantly outperforms existing state-of-the-art reference-based methods, exhibiting superior accuracy and a stronger ability to capture fine-grained perceptual cues and SR-specific artifacts. The SQer method provides a promising direction for guiding the optimization and application of image super-resolution models.