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.

翻译：传统的图像质量评估（IQA）指标通常落入两个极端之一：要么是刚性的、手工设计的数学模型，要么是完全缺乏可解释性的“黑箱”深度学习架构。为了弥合这一差距，我们提出了EvoIQA，这是一个基于遗传编程的完全可解释的符号回归框架，用于演化出明确的、人类可读的图像质量评估（IQA）数学公式。通过利用来自VSI、VIF、FSIM和HaarPSI指标的丰富终端集，我们的框架将结构、色彩和信息论层面的退化固有地映射为可观测的数学方程。我们的结果表明，演化出的GP模型始终能在预测结果与人类视觉偏好之间实现强一致性。此外，这些模型不仅优于传统手工设计的指标，还能与DB-CNN等复杂的最先进深度学习模型达到性能持平，证明了我们无需再为了追求最先进性能而牺牲可解释性。