Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment

• URL: http://arxiv.org/abs/2411.09007v1
• Date: Wed, 13 Nov 2024 20:17:30 GMT
• Title: Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
• Authors: Zihao Huang, Xudong Li, Bohan Fu, Xiaohui Chu, Ke Li, Yunhang Shen, Yan Zhang,
• Abstract summary: Blind image quality assessment (BIQA) serves as a fundamental task in computer vision, yet it often fails to consistently align with human subjective perception. Recent advances show that multi-scale evaluation strategies are promising due to their ability to replicate the hierarchical structure of human vision. This paper addresses two primary challenges: the significant redundancy of information across different scales, and the confusion caused by combining features from these scales.
• Score: 15.235786583920062
• License:
• Abstract: Blind image quality assessment (BIQA) serves as a fundamental task in computer vision, yet it often fails to consistently align with human subjective perception. Recent advances show that multi-scale evaluation strategies are promising due to their ability to replicate the hierarchical structure of human vision. However, the effectiveness of these strategies is limited by a lack of understanding of how different image scales influence perceived quality. This paper addresses two primary challenges: the significant redundancy of information across different scales, and the confusion caused by combining features from these scales, which may vary widely in quality. To this end, a new multi-scale BIQA framework is proposed, namely Contrast-Constrained Scale-Focused IQA Framework (CSFIQA). CSFIQA features a selective focus attention mechanism to minimize information redundancy and highlight critical quality-related information. Additionally, CSFIQA includes a scale-level contrastive learning module equipped with a noise sample matching mechanism to identify quality discrepancies across the same image content at different scales. By exploring the intrinsic relationship between image scales and the perceived quality, the proposed CSFIQA achieves leading performance on eight benchmark datasets, e.g., achieving SRCC values of 0.967 (versus 0.947 in CSIQ) and 0.905 (versus 0.876 in LIVEC).

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