Beyond Ground-Truth Fidelity: Rethinking Exposure Correction through Input-Conditioned Perceptual Evaluation

Prevailing evaluation protocols for exposure correction (EC) prioritize fidelity to a designated ground truth (GT), implicitly treating the GT as a perfect perceptual reference. Yet a mis-exposed input can retain reliable scene content absent from the GT, raising the question of whether higher target fidelity necessarily implies a better correction. To examine this premise, we conduct an extensive human preference study with more than 30 observers, collecting over 60,000 pairwise judgments over 5,200 images. We generate visually diverse candidate corrections using representative EC models trained with different objectives that induce distinct trade-offs in exposure, color, and content preservation. In each trial, observers view the input alongside two candidate corrections and provide pairwise preferences among all three images. The resulting judgments reveal limited agreement between conventional metric rankings and human preferences, motivating us to reformulate EC quality in terms of input-conditioned perceptual improvement. We construct ECIQA-Pref from these judgments and develop ECIQA, a degraded-reference metric that models directed input–candidate relations using global appearance and native-resolution local structural evidence. ECIQA achieves the highest agreement with subjective scores among the evaluated full-, no-, and degraded-reference metrics, reaching 0.6482 SRCC and 0.8169 PLCC. A qualitative pilot study further suggests that ECIQA-guided training can better preserve input-visible structure, providing preliminary support for optimizing EC beyond target fidelity.