High-frequency distortion detection in evaluating light field quality via axially symmetric dual-fan filtering

The growing adoption of light field imaging in computational photography, autonomous driving, and immersive display systems has created an urgent need for accurate quality assessment. While Gabor-based methods can effectively analyze texture through multi-scale representations, their conventional fixed-scale implementations lack adaptability to varying distortion levels, resulting in compromised accuracy and efficiency. To address this limitation, we propose an adaptive-scale Gabor framework for light field image quality assessment (LFIQA) that dynamically adjusts feature extraction according to distortion severity. Specifically, we construct an axially symmetric dual-fan filter that first quantifies high-frequency distortion in the Fourier domain. Then, we map the quantified high-frequency distortion to optimal Gabor scales through an exponential transform, enabling distortion-adaptive feature extraction. By computing structural similarity of these adaptive Gabor features, our method achieves state-of-the-art performance across three benchmark datasets. Notably,our distortion-adaptive mechanism reduces computational redundancy compared to conventional multi-scale approaches. To our knowledge, this is the first LFIQA method that successfully integrates frequency-domain distortion quantification with adaptive Gabor scaling, offering both superior accuracy and practical efficiency for real-word applications.