Enhancing Underwater Image Quality Assessment with Influential Perceptual Features
In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effect...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/23/4760 |
| _version_ | 1797400254172102656 |
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| author | Feifei Liu Zihao Huang Tianrang Xie Runze Hu Bingbing Qi |
| author_facet | Feifei Liu Zihao Huang Tianrang Xie Runze Hu Bingbing Qi |
| author_sort | Feifei Liu |
| collection | DOAJ |
| description | In the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment. |
| first_indexed | 2024-03-09T01:52:53Z |
| format | Article |
| id | doaj.art-676db4dc37aa4495944539953a103be8 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T01:52:53Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-676db4dc37aa4495944539953a103be82023-12-08T15:13:54ZengMDPI AGElectronics2079-92922023-11-011223476010.3390/electronics12234760Enhancing Underwater Image Quality Assessment with Influential Perceptual FeaturesFeifei Liu0Zihao Huang1Tianrang Xie2Runze Hu3Bingbing Qi4Changshu Institute of Technology, Suzhou 215556, ChinaSchool of Business and Commerce, Southwest University, Chongqing 400715, ChinaBeijing Institute of Technology, Beijing 100811, ChinaBeijing Institute of Technology, Beijing 100811, ChinaBeijing Institute of Technology, Beijing 100811, ChinaIn the multifaceted field of oceanic engineering, the quality of underwater images is paramount for a range of applications, from marine biology to robotic exploration. This paper presents a novel approach in underwater image quality assessment (UIQA) that addresses the current limitations by effectively combining low-level image properties with high-level semantic features. Traditional UIQA methods predominantly focus on either low-level attributes such as brightness and contrast or high-level semantic content, but rarely both, which leads to a gap in achieving a comprehensive assessment of image quality. Our proposed methodology bridges this gap by integrating these two critical aspects of underwater imaging. We employ the least-angle regression technique for balanced feature selection, particularly in high-level semantics, to ensure that the extensive feature dimensions of high-level content do not overshadow the fundamental low-level properties. The experimental results of our method demonstrate a remarkable improvement over existing UIQA techniques, establishing a new benchmark in both accuracy and reliability for underwater image assessment.https://www.mdpi.com/2079-9292/12/23/4760image quality assessmentvision transformerlow-levelfeature selection |
| spellingShingle | Feifei Liu Zihao Huang Tianrang Xie Runze Hu Bingbing Qi Enhancing Underwater Image Quality Assessment with Influential Perceptual Features Electronics image quality assessment vision transformer low-level feature selection |
| title | Enhancing Underwater Image Quality Assessment with Influential Perceptual Features |
| title_full | Enhancing Underwater Image Quality Assessment with Influential Perceptual Features |
| title_fullStr | Enhancing Underwater Image Quality Assessment with Influential Perceptual Features |
| title_full_unstemmed | Enhancing Underwater Image Quality Assessment with Influential Perceptual Features |
| title_short | Enhancing Underwater Image Quality Assessment with Influential Perceptual Features |
| title_sort | enhancing underwater image quality assessment with influential perceptual features |
| topic | image quality assessment vision transformer low-level feature selection |
| url | https://www.mdpi.com/2079-9292/12/23/4760 |
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