Learning Keypoint-Based Structural Metrics for Super-Resolution Image Quality Assessment

Assessing the quality of super-resolved images is important for comparing reconstruction algorithms, but pixel fidelity, perceptual appearance, and structural preservation do not always agree. We investigate whether keypoint detector response maps and detected keypoints can act as trainable structural indicators for aligned full-reference super-resolution image quality assessment (SR-IQA). A contrastive Multi-Scale Index Proposal (MSIP) objective specializes Key.Net toward SR-like resolution loss, candidate checkpoints are screened without subjective labels, and six response- and keypoint-based measures are evaluated on four subjective benchmarks. The results show a redistribution rather than a uniform improvement: MSIP correlations increase on three of the four benchmarks, both repeatability variants decrease for every trained family on SISAR and RealSRQ, and general keypoint performance on HPatches decreases for trained checkpoints. At the benchmark level, established comparators such as TOPIQ-FR, RQI, and DISQ remain stronger, and on three of the four datasets the best keypoint-based result is still obtained with the pretrained detector. The measures nevertheless retain quality-related variation after conditioning on ten IQA controls in 88 of 120 tested hypotheses, with markedly weaker evidence on RealSRQ. Their practical value is therefore diagnostic: the response maps localize the structures behind an HR–SR discrepancy, complementing rather than replacing established SR-IQA metrics.