Contrastive Local Manifold Learning for No-Reference Image Quality Assessment

• URL: http://arxiv.org/abs/2406.19247v2
• Date: Mon, 13 Oct 2025 16:07:02 GMT
• Title: Contrastive Local Manifold Learning for No-Reference Image Quality Assessment
• Authors: Zihao Huang, Runze Hu, Timin Gao, Yan Zhang, Yunhang Shen, Ke Li,
• Abstract summary: LML-IQA is an innovative no-reference IQA (NR-IQA) approach that leverages a combination of local manifold learning and contrastive learning.<n>Our approach first extracts multiple patches from each image and identifies the most visually salient region.<n>Experiments across eight benchmark datasets demonstrate significant performance gains over state-of-the-art methods.
• Score: 30.346699251783733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image Quality Assessment (IQA) methods typically overlook local manifold structures, leading to compromised discriminative capabilities in perceptual quality evaluation. To address this limitation, we present LML-IQA, an innovative no-reference IQA (NR-IQA) approach that leverages a combination of local manifold learning and contrastive learning. Our approach first extracts multiple patches from each image and identifies the most visually salient region. This salient patch serves as a positive sample for contrastive learning, while other patches from the same image are treated as intra-class negatives to preserve local distinctiveness. Patches from different images also act as inter-class negatives to enhance feature separation. Additionally, we introduce a mutual learning strategy to improve the model's ability to recognize and prioritize visually important regions. Comprehensive experiments across eight benchmark datasets demonstrate significant performance gains over state-of-the-art methods, achieving a PLCC of 0.942 on TID2013 (compared to 0.908) and 0.977 on CSIQ (compared to 0.965).

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