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Abstract
Owing to the complexity of the underwater environment and the limitations of imaging devices, the quality of underwater images varies differently, which may affect the practical applications in modern military, scientific research, and other fields. Thus, achieving subjective quality assessment to distinguish different qualities of underwater images has an important guiding role for subsequent tasks. In this paper, considering the underwater image degradation effect and human visual perception scheme, an effective reference-free underwater image quality assessment metric is designed by combining the colorfulness, contrast, and sharpness cues. Specifically, inspired by the different sensibility of humans to high-frequency and low-frequency information, we design a more comprehensive color measurement in spatial domain and frequency domain. In addition, for the low contrast caused by the backward scattering, we propose a dark channel prior weighted contrast measure to enhance the discrimination ability of the original contrast measurement. The sharpness measurement is used to evaluate the blur effect caused by the forward scattering of the underwater image. Finally, these three measurements are combined by the weighted summation, where the weighed coefficients are obtained by multiple linear regression. Moreover, we collect a large dataset for underwater image quality assessment for testing and evaluating different methods. Experiments on this dataset demonstrate the superior performance both qualitatively and quantitatively.
| Original language | English |
|---|---|
| Article number | 116218 |
| Journal | Signal Processing: Image Communication |
| Volume | 94 |
| Online published | 17 Mar 2021 |
| DOIs | |
| Publication status | Published - May 2021 |
Research Keywords
- Dark channel prior weighting
- Frequency domain
- New dataset
- Reference-free image quality assessment
- Underwater image
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Dive into the research topics of 'A reference-free underwater image quality assessment metric in frequency domain'. Together they form a unique fingerprint.Projects
- 1 Finished
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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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