High Dynamic Range Image Quality Assessment Based on Frequency Disparity

TL;DR

This paper introduces a new HDR image quality assessment method based on frequency disparity, leveraging local-global frequency features to better match human visual perception and outperform existing methods.

Contribution

The paper proposes the LGFM model that uses Gabor and Butterworth filters for local and global frequency feature extraction in HDR images, improving quality prediction accuracy.

Findings

LGFM achieves higher consistency with subjective perception.

Outperforms state-of-the-art HDR IQA methods on benchmark datasets.

Effective use of frequency features for HDR image quality assessment.

Abstract

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}.