# High Dynamic Range Image Quality Assessment Based on Frequency Disparity

arXiv: https://arxiv.org/abs/2209.02285 · HTML: https://mlsanity.com/paper/2209.02285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content](https://mlsanity.com/paper/1803.02266/) (2018)
- [No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness](https://mlsanity.com/paper/2102.11393/) (2021)
- [Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality](https://mlsanity.com/paper/2012.10758/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity](https://mlsanity.com/paper/2506.12505/) (2025)

## Abstract (arXiv)

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: urlhttps://github.com/eezkni/LGFM.