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Review
. 2024 Sep 27;10(10):243.
doi: 10.3390/jimaging10100243.

Overview of High-Dynamic-Range Image Quality Assessment

Yue Liu  1 , Yu Tian  1 , Shiqi Wang  1 , Xinfeng Zhang  2 , Sam Kwong  3
Affiliations
Review

Overview of High-Dynamic-Range Image Quality Assessment

Yue Liu et al. J Imaging. .

Abstract

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

Keywords: high dynamic range; objective image quality assessment; subjective image quality assessment.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 4
Figure 4
Overview of objective HDR IQA methods. The HDR images presented in this paper are tone-mapped for visualization by Gamma correction.
Figure 1
Figure 1
Overview of the HDR image compression schemes.
Figure 2
Figure 2
The production process of the LDR image in the sensor.
Figure 3
Figure 3
Average histogram of the HDR and LDR images in the SIHDR [52] database.
Figure 5
Figure 5
Statistical analysis using the left-tail F-test. Cells highlighted in green (with a value of 1) indicate that the model corresponding to the row significantly outperforms the model corresponding to the column, while a value of 0 indicates no significant difference.
See this image and copyright information in PMC

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