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Abstract
Evaluating the quality of high dynamic range (HDR) images has emerged as a challenging and contemporary topic with the proliferation of HDR content. In this work, a new HDR compression (HDRC) database is proposed, aiming to provide a benchmark for the development of full-reference HDR image quality assessment (IQA) algorithms when facing the latest HDR compression distortions. In particular, the proposed HDRC database is the first HDR-IQA database to incorporate Versatile Video Coding (VVC) compression distortions, closely associated with practical application scenarios. Furthermore, the proposed HDRC database is currently the largest HDR-IQA database, including 80 reference images and 400 distorted images. Extensive experiments are conducted by studying the performance compared to existing HDR-IQA databases when evaluating three HDR-specific IQA models and nine IQA models prevalent for low dynamic range (LDR) content, revealing the challenges the proposed HDRC database brings. The results indicate that the existing IQA models demonstrate noticeable decreases in accuracy when assessing new compression distortions, underscoring the need for the development of novel HDR-IQA models. Consequently, the suggested HDRC database can serve as a potential database for HDR-IQA research, fostering a comprehensive exploration of the associated fields. © The Author(s) 2024.
| Original language | English |
|---|---|
| Pages (from-to) | 4373-4388 |
| Journal | International Journal of Machine Learning and Cybernetics |
| Volume | 15 |
| Issue number | 10 |
| Online published | 6 May 2024 |
| DOIs | |
| Publication status | Published - Oct 2024 |
Funding
Open Access Publishing Support Fund provided by Lingnan University. This work in part supported by the Hong Kong GRFRGC General Research Fund under Grant 11203820 and Grant 11209819.
Research Keywords
- High dynamic range
- Image quality assessment
- Subjective test
Publisher's Copyright Statement
- This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/
RGC Funding Information
- RGC-funded
Fingerprint
Dive into the research topics of 'HDRC: a subjective quality assessment database for compressed high dynamic range image'. Together they form a unique fingerprint.Projects
- 2 Finished
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GRF: Adaptive Dynamic Range Enhancement Oriented to High Dynamic Display
KWONG, T. W. S. (Principal Investigator / Project Coordinator), KUO, J. (Co-Investigator), WANG, S. (Co-Investigator) & Zhang, X. (Co-Investigator)
1/01/21 → 5/09/23
Project: Research
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GRF: Intelligent Ultra High Definition Video Encoder Optimization for Future Versatile Video Coding
KWONG, T. W. S. (Principal Investigator / Project Coordinator), KUO, J. (Co-Investigator), WANG, S. (Co-Investigator) & ZHOU, M. (Co-Investigator)
1/01/20 → 5/09/23
Project: Research
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