用泰勒展开提升图像质量评估模型的精度与效率
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
- 以泰勒展开替代传统激活函数，增强局部拟合能力
- 在5个数据集上均优于其他KAN模型，跨库泛化能力强
- 适合需要高效高精度图像质量评估的场景
Kolmogorov-Arnold网络（KAN）因其强大的函数逼近能力受到关注。此前研究将KAN用于盲图像质量评估（BIQA）的得分回归，但处理高维特征时存在性能提升有限、计算成本高的问题。为此，本文提出基于泰勒展开的TaylorKAN，利用泰勒展开作为可学习激活函数，增强局部逼近能力；同时在得分回归流程中引入网络深度缩减与特征维度压缩，提升计算效率。在包含真实失真的五个数据库（BID、CLIVE、KonIQ、SPAQ、FLIVE）上的实验表明，TaylorKAN始终优于其他KAN相关模型，证明基于泰勒展开的局部逼近比基于正交函数的全局逼近更有效。跨数据库实验验证了其良好的泛化能力。结果表明，TaylorKAN是一种高效且鲁棒的高维得分回归模型。
原文摘要 · Abstract (English)
Kolmogorov-Arnold Network (KAN) has attracted growing interest for its strong function approximation capability. In our previous work, KAN and its variants were explored in score regression for blind image quality assessment (BIQA). However, these models encounter challenges when processing high-dimensional features, leading to limited performance gains and increased computational cost. To address these issues, we propose TaylorKAN that leverages the Taylor expansions as learnable activation functions to enhance local approximation capability. To improve the computational efficiency, network depth reduction and feature dimensionality compression are integrated into the TaylorKAN-based score regression pipeline. On five databases (BID, CLIVE, KonIQ, SPAQ, and FLIVE) with authentic distortions, extensive experiments demonstrate that TaylorKAN consistently outperforms the other KAN-related models, indicating that the local approximation via Taylor expansions is more effective than global approximation using orthogonal functions. Its generalization capacity is validated through inter-database experiments. The findings highlight the potential of TaylorKAN as an efficient and robust model for high-dimensional score regression.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。