Despite substantial progress in no-reference image quality assessment (NR-IQA), previous training models often suffer from over-fitting due to the limited scale of used datasets, resulting in model performance bottlenecks. To tackle this challenge, we explore the potential of leveraging data augmentation to improve data efficiency and enhance model robustness. However, most existing data augmentation methods incur a serious issue, namely that it alters the image quality and leads to training images mismatching with their original labels. Additionally, although only a few data augmentation methods are available for NR-IQA task, their ability to enrich dataset diversity is still insufficient. To address these issues, we propose a effective and general data augmentation based on just noticeable difference (JND) noise mixing for NR-IQA task, named JNDMix. In detail, we randomly inject the JND noise, imperceptible to the human visual system (HVS), into the training image without any adjustment to its label. Extensive experiments demonstrate that JNDMix significantly improves the performance and data efficiency of various state-of-the-art NR-IQA models and the commonly used baseline models, as well as the generalization ability. More importantly, JNDMix facilitates MANIQA to achieve the state-of-the-art performance on LIVEC and KonIQ-10k.

翻译：尽管无参考图像质量评估（NR-IQA）取得了显著进展，但由于所用数据集规模有限，以往的训练模型常受限于过拟合问题，导致模型性能出现瓶颈。为应对这一挑战，我们探索利用数据增强提升数据效率与模型鲁棒性的潜力。然而，现有大多数数据增强方法存在严重问题，即会改变图像质量，导致训练图像与其原始标签不匹配。此外，尽管针对NR-IQA任务仅有少量数据增强方法可用，但这些方法在丰富数据集多样性方面的能力仍显不足。为解决上述问题，我们提出一种基于恰可察觉差异（JND）噪声混合的高效通用数据增强方法JNDMix，专门用于NR-IQA任务。具体而言，我们将人眼视觉系统（HVS）不可感知的JND噪声随机注入训练图像，且无需调整其标签。大量实验表明，JNDMix显著提升了多种最新NR-IQA模型及常用基线模型的性能、数据效率以及泛化能力。更重要的是，JNDMix助力MANIQA在LIVEC和KonIQ-10k数据集上达到了最先进的性能水平。