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Despite substantial progress in no-reference image quality assessment(NR-IQA), previous training models often suffer from over-fitting due to thelimited scale of used datasets, resulting in model performance bottlenecks. Totackle this challenge, we explore the potential of leveraging data augmentationto improve data efficiency and enhance model robustness. However, most existingdata augmentation methods incur a serious issue, namely that it alters theimage quality and leads to training images mismatching with their originallabels. Additionally, although only a few data augmentation methods areavailable for NR-IQA task, their ability to enrich dataset diversity is stillinsufficient. To address these issues, we propose a effective and general dataaugmentation based on just noticeable difference (JND) noise mixing for NR-IQAtask, named JNDMix. In detail, we randomly inject the JND noise, imperceptibleto the human visual system (HVS), into the training image without anyadjustment to its label. Extensive experiments demonstrate that JNDMixsignificantly improves the performance and data efficiency of variousstate-of-the-art NR-IQA models and the commonly used baseline models, as wellas the generalization ability. More importantly, JNDMix facilitates MANIQA toachieve the state-of-the-art performance on LIVEC and KonIQ-10k.

JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment