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arXiv · 2406.19247

Contrastive Local Manifold Learning for No-Reference Image Quality Assessment

Zihao Huang·Runze Hu·Timin Gao·Yan Zhang·Yunhang Shen·Ke Li
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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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Zihao Huang, Runze Hu, Timin Gao, Yan Zhang, Yunhang Shen, Ke Li. 2024-06-27. Contrastive Local Manifold Learning for No-Reference Image Quality Assessment. https://arxiv.org/abs/2406.19247

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