Existing full-reference image quality assessment (FR-IQA) methods achieve high-precision evaluation by analysing feature differences between reference and distorted images. However, their performance is constrained by the quality of the reference image, which limits real-world applications where ideal reference sources are unavailable. Notably, the human visual system has the ability to accumulate visual memory, allowing image quality assessment on the basis of long-term memory storage. Inspired by this biological memory mechanism, we propose a memory-driven quality-aware framework (MQAF), which establishes a memory bank for storing distortion patterns and dynamically switches between dual-mode quality assessment strategies to reduce reliance on high-quality reference images. When reference images are available, MQAF obtains reference-guided quality scores by adaptively weighting reference information and comparing the distorted image with stored distortion patterns in the memory bank. When the reference image is absent, the framework relies on distortion patterns in the memory bank to infer image quality, enabling no-reference quality assessment (NR-IQA). The experimental results show that our method outperforms state-of-the-art approaches across multiple datasets while adapting to both no-reference and full-reference tasks.

翻译：现有的全参考图像质量评估（FR-IQA）方法通过分析参考图像与失真图像之间的特征差异实现高精度评估。然而，其性能受限于参考图像的质量，这在理想参考源不可用的实际应用场景中受到制约。值得注意的是，人类视觉系统具备积累视觉记忆的能力，能够基于长期记忆存储进行图像质量评估。受此生物记忆机制启发，我们提出一种记忆驱动的质量感知框架（MQAF），该框架建立用于存储失真模式的记忆库，并通过动态切换双模式质量评估策略来降低对高质量参考图像的依赖。当参考图像可用时，MQAF通过自适应加权参考信息，并将失真图像与记忆库中存储的失真模式进行比较，从而获得参考引导的质量分数。当参考图像缺失时，该框架依赖记忆库中的失真模式推断图像质量，实现无参考质量评估（NR-IQA）。实验结果表明，我们的方法在多个数据集上优于现有先进方法，同时能适应无参考和全参考两类任务。