用进化算法生成可解释的图像质量评估公式，既准确又看得懂。
EvoIQA - Explaining Image Distortions with Evolved White-Box Logic
- 基于遗传编程演化出人类可读的数学公式来评估图像质量。
- 在人眼偏好一致性上优于传统公式，媲美顶尖深度学习模型。
- 适合需要透明决策过程的医疗、质检等可信场景。
传统图像质量评估（IQA）方法通常分为两类：僵化的手工公式或完全不可解释的黑盒深度学习模型。为弥合这一差距，我们提出EvoIQA，一种基于遗传编程的可解释符号回归框架，能够演化出显式且人类可读的数学公式用于图像质量评估。该框架利用来自VSI、VIF、FSIM和HaarPSI等指标的丰富终端集，将结构、色彩和信息论退化自然映射为可观测的数学表达式。实验表明，演化出的GP模型在预测结果与人类视觉偏好之间表现出强一致性。此外，其性能不仅超越传统手工设计的度量，还达到与复杂前沿深度学习模型（如DB-CNN）相当的水平，证明在不牺牲可解释性的情况下仍可实现最先进性能。
原文摘要 · Abstract (English)
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 consistently achieve strong alignment between the predictions and human visual preferences. Furthermore, they not only outperform traditional hand-crafted metrics but also achieve performance parity with complex, state-of-the-art deep learning models like DB-CNN, proving that we no longer have to sacrifice interpretability for state-of-the-art performance.
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