Image Quality Assessment (IQA) remains an unresolved challenge in the field of computer vision, due to complex distortion conditions, diverse image content, and limited data availability. The existing Blind IQA (BIQA) methods heavily rely on extensive human annotations to train models, which is both labor-intensive and costly due to the demanding nature of creating IQA datasets. To mitigate the dependence on labeled samples, this paper introduces a novel Gradient-Regulated Meta-Prompt IQA Framework (GRMP-IQA). This framework aims to fast adapt the powerful visual-language pre-trained model, CLIP, to downstream IQA tasks, significantly improving accuracy in scenarios with limited data. Specifically, the GRMP-IQA comprises two key modules: Meta-Prompt Pre-training Module and Quality-Aware Gradient Regularization. The Meta Prompt Pre-training Module leverages a meta-learning paradigm to pre-train soft prompts with shared meta-knowledge across different distortions, enabling rapid adaptation to various IQA tasks. On the other hand, the Quality-Aware Gradient Regularization is designed to adjust the update gradients during fine-tuning, focusing the model's attention on quality-relevant features and preventing overfitting to semantic information. Extensive experiments on five standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods under limited data setting, i.e., achieving SRCC values of 0.836 (vs. 0.760 on LIVEC) and 0.853 (vs. 0.812 on KonIQ). Notably, utilizing just 20\% of the training data, our GRMP-IQA outperforms most existing fully supervised BIQA methods.

翻译：图像质量评估（IQA）是计算机视觉领域一个尚未解决的挑战，这主要源于复杂的失真条件、多样的图像内容以及有限的数据可用性。现有的盲图像质量评估（BIQA）方法严重依赖大量人工标注来训练模型，而由于构建IQA数据集的严苛要求，这一过程既费力又昂贵。为了减轻对标注样本的依赖，本文提出了一种新颖的梯度调控元提示IQA框架（GRMP-IQA）。该框架旨在快速适应强大的视觉语言预训练模型CLIP至下游IQA任务，从而在数据有限的情况下显著提升评估精度。具体而言，GRMP-IQA包含两个关键模块：元提示预训练模块和质量感知梯度正则化模块。元提示预训练模块利用元学习范式，通过跨不同失真类型共享的元知识对软提示进行预训练，使其能够快速适应各种IQA任务。另一方面，质量感知梯度正则化模块旨在调整微调过程中的更新梯度，使模型聚焦于与质量相关的特征，并防止其过度拟合语义信息。在五个标准BIQA数据集上的大量实验表明，本方法在有限数据设置下性能优于当前最先进的BIQA方法，例如在LIVEC数据集上获得了0.836的SRCC值（对比基准为0.760），在KonIQ数据集上获得了0.853的SRCC值（对比基准为0.812）。值得注意的是，仅使用20%的训练数据，我们的GRMP-IQA模型性能已超越大多数现有的全监督BIQA方法。