Local Manifold Learning for No-Reference Image Quality Assessment

• URL: http://arxiv.org/abs/2406.19247v1
• Date: Thu, 27 Jun 2024 15:14:23 GMT
• Title: Local Manifold Learning for No-Reference Image Quality Assessment
• Authors: Timin Gao, Wensheng Pan, Yan Zhang, Sicheng Zhao, Shengchuan Zhang, Xiawu Zheng, Ke Li, Liujuan Cao, Rongrong Ji,
• Abstract summary: We propose an innovative framework that integrates local manifold learning with contrastive learning for No-Reference Image Quality Assessment (NR-IQA) Our approach demonstrates a better performance compared to state-of-the-art methods in 7 standard datasets.
• Score: 68.9577503732292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning has considerably advanced the field of Image Quality Assessment (IQA), emerging as a widely adopted technique. The core mechanism of contrastive learning involves minimizing the distance between quality-similar (positive) examples while maximizing the distance between quality-dissimilar (negative) examples. Despite its successes, current contrastive learning methods often neglect the importance of preserving the local manifold structure. This oversight can result in a high degree of similarity among hard examples within the feature space, thereby impeding effective differentiation and assessment. To address this issue, we propose an innovative framework that integrates local manifold learning with contrastive learning for No-Reference Image Quality Assessment (NR-IQA). Our method begins by sampling multiple crops from a given image, identifying the most visually salient crop. This crop is then used to cluster other crops from the same image as the positive class, while crops from different images are treated as negative classes to increase inter-class distance. Uniquely, our approach also considers non-saliency crops from the same image as intra-class negative classes to preserve their distinctiveness. Additionally, we employ a mutual learning framework, which further enhances the model's ability to adaptively learn and identify visual saliency regions. Our approach demonstrates a better performance compared to state-of-the-art methods in 7 standard datasets, achieving PLCC values of 0.942 (compared to 0.908 in TID2013) and 0.914 (compared to 0.894 in LIVEC).

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