HDRC : a subjective quality assessment database for compressed high dynamic range image

Yue LIU, Zhangkai NI, Peilin CHEN, Shiqi WANG, Sam KWONG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


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.
Original languageEnglish
JournalInternational Journal of Machine Learning and Cybernetics
Publication statusE-pub ahead of print - 6 May 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.


  • High dynamic range
  • Image quality assessment
  • Subjective test


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