EvoIQA - Explaining Image Distortions with Evolved White-Box Logic
Traditional Image Quality Assessment (IQA) metrics typically fall into one of two extremes: rigid, hand-crafted mathematical models or "black-box" deep learning architectures that completely lack interpretability. To bridge this gap, we propose EvoIQA, a fully explainable symbolic regression framework based on Genetic Programming that Evolves explicit, human-readable mathematical formulas for image quality assessment (IQA). Utilizing a rich terminal set from the VSI, VIF, FSIM, and HaarPSI metrics, our framework inherently maps structural, chromatic, and information-theoretic degradations into observable mathematical equations. Our results demonstrate that the evolved GP models consistent...
| Authors | Ruchika Gupta, Illya Bakurov, Nathan Haut et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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