Year: 2025
Type: article
Abstract: Critical details in both bright and dark regions are frequently lost in high dynamic range (HDR) images when they are displayed on low dynamic range (LDR) devices. To mitigate this issue, tone mapping operators (TMOs) have been developed to convert HDR images into LDR representations while maintaining perceptual quality. However, it is challenging to effectively balance various key visual attributes, such as naturalness and structural fidelity. To overcome this limitation, a two-stage Bayesian optimization approach was proposed in this work to enhance the perceptual quality of tone-mapped images across multiple evaluation metrics. The first stage adaptively optimizes TMQI parameters to capture image-specific perceptual characteristics, while the second stage refines the tone mapping function to further improve detail preservation and visual realism. Extensive experiments using three distinct HDR benchmark datasets were conducted, indicating that the proposed method generally performs better than the existing tone mapping techniques across most evaluated metrics, including TMQI, Naturalness, and Structural Fidelity. Our adaptive approach offers a robust and effective solution for optimizing HDR image conversion, resulting in a significantly improved perceptual quality compared to traditional methods.
Source: Electronics
Author: Naif Alasmari
Institution: King Khalid University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Funder: King Khalid University
OpenAlex ID: W4415299584