  In this paper, a novel and effective image quality assessment (IQA) algorithm
based on frequency disparity for high dynamic range (HDR) images is proposed,
termed as local-global frequency feature-based model (LGFM). Motivated by the
assumption that the human visual system is highly adapted for extracting
structural information and partial frequencies when perceiving the visual
scene, the Gabor and the Butterworth filters are applied to the luminance of
the HDR image to extract local and global frequency features, respectively. The
similarity measurement and feature pooling are sequentially performed on the
frequency features to obtain the predicted quality score. The experiments
evaluated on four widely used benchmarks demonstrate that the proposed LGFM can
provide a higher consistency with the subjective perception compared with the
state-of-the-art HDR IQA methods. Our code is available at:
\url{https://github.com/eezkni/LGFM}.
