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<p>No-reference image quality assessment (NR-IQA) is a pivotal technique for evaluating visual content quality across a wide spectrum of applications, from social media and remote education to autonomous driving and medical imaging. Despite significant advancements in deep learning-based NR-IQA methods, two critical challenges persist: heavy reliance on large-scale labeled datasets and insufficient ability to perceive diverse distortions. To address these limitations, we propose a two-stage framework integrating degradation-aware self-supervised contrastive pre-training with frequency-spatial domain feature learning. In the first stage, a MoCo-based contrastive learning strategy trains a momentum encoder to learn robust, generalizable distortion representations by distinguishing between differently degraded image pairs, eliminating the need for subjective quality labels. The second stage introduces a frequency-spatial perception module that conducts complementary analysis: in the frequency domain, a contrast sensitivity function-weighted spectral analysis emphasizes perceptually relevant artifacts, while in the spatial domain, multi-scale features are extracted via parallel convolutional branches, with adaptive fusion of all features through an attention mechanism. Here we show that extensive experiments on seven benchmark datasets (LIVE, CSIQ, TID2013, KADID-10K, LIVEC, KonIQ-10K, LIVE-FB) demonstrate superior performance, achieving a maximum PLCC of 0.958 and SROCC of 0.955 on KADID-10K, and 0.955 PLCC and 0.939 SROCC on KonIQ-10K. This framework not only mitigates the reliance on labeled data but also enhances the generalization across synthetic and authentic distortions, offering a reliable solution for NR-IQA in real-world multimedia systems and advancing the alignment between computational quality assessment and human visual perception. The source code is available at <a href="https://github.com/huqigong/DFS">https://github.com/huqigong/DFS</a>.</p>

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Degradation-aware contrastive learning with frequency-spatial fusion: a two-stage framework for no-reference image quality assessment

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Litao Duan,
  • Yan Huang,
  • Yong Xu,
  • Le Callet Patrick

摘要

No-reference image quality assessment (NR-IQA) is a pivotal technique for evaluating visual content quality across a wide spectrum of applications, from social media and remote education to autonomous driving and medical imaging. Despite significant advancements in deep learning-based NR-IQA methods, two critical challenges persist: heavy reliance on large-scale labeled datasets and insufficient ability to perceive diverse distortions. To address these limitations, we propose a two-stage framework integrating degradation-aware self-supervised contrastive pre-training with frequency-spatial domain feature learning. In the first stage, a MoCo-based contrastive learning strategy trains a momentum encoder to learn robust, generalizable distortion representations by distinguishing between differently degraded image pairs, eliminating the need for subjective quality labels. The second stage introduces a frequency-spatial perception module that conducts complementary analysis: in the frequency domain, a contrast sensitivity function-weighted spectral analysis emphasizes perceptually relevant artifacts, while in the spatial domain, multi-scale features are extracted via parallel convolutional branches, with adaptive fusion of all features through an attention mechanism. Here we show that extensive experiments on seven benchmark datasets (LIVE, CSIQ, TID2013, KADID-10K, LIVEC, KonIQ-10K, LIVE-FB) demonstrate superior performance, achieving a maximum PLCC of 0.958 and SROCC of 0.955 on KADID-10K, and 0.955 PLCC and 0.939 SROCC on KonIQ-10K. This framework not only mitigates the reliance on labeled data but also enhances the generalization across synthetic and authentic distortions, offering a reliable solution for NR-IQA in real-world multimedia systems and advancing the alignment between computational quality assessment and human visual perception. The source code is available at https://github.com/huqigong/DFS.