Blind Quality Assessment of Light Field Images via Fourier Channel Attention and Multi-Scale Fusion

Developing robust Light Field Image Quality Assessment (LFIQA) methods is essential for advancing light field imaging technologies and enhancing immersive visual experiences. However, existing blind LFIQA methods often lack accuracy and performance, failing to adequately capture the multi-dimensional aspects of light field image (LFI) quality. To address these limitations, we propose a novel blind LFIQA model that incorporates a Fourier Channel Attention Block (FCAB) and multi-scale feature fusion. The FCAB enhances feature extraction through the Fourier transform in the frequency domain, enabling the capture of more representative quality-related information. Additionally, the model incorporates multi-scale feature maps with varying receptive fields through a progressive fusion strategy, enabling a comprehensive evaluation of diverse quality dimensions. To further improve regression accuracy, we employ Kolmogorov-Arnold Networks (KANs), which outperform traditional Multi-Layer Perceptrons (MLPs). Extensive experimental results show that the proposed model surpasses existing LFIQA methods.