Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment

TL;DR

TaylorKAN introduces Taylor expansion-based learnable activations to improve local approximation and efficiency in blind image quality assessment, outperforming previous models on multiple datasets.

Contribution

The paper proposes TaylorKAN, a novel KAN variant using Taylor expansions for enhanced local approximation and reduced computational complexity in BIQA.

Findings

Outperforms existing KAN models on five databases.

Demonstrates superior generalization across different datasets.

Achieves better accuracy with lower computational cost.

Abstract

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.