Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective
The Internet of Underwater Things (IoUT) is a network of countless connected devices that monitor vast, uncharted water territories. These gadgets consists of cameras designed to capture images beneath the water’s surface. and then distribute it among themselves and save them in the cloud...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10912481/ |
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| author | R. Monika Samiappan Dhanalakshmi R. Narayanamoorthi Hossam Kotb Amr Yousef |
| author_facet | R. Monika Samiappan Dhanalakshmi R. Narayanamoorthi Hossam Kotb Amr Yousef |
| author_sort | R. Monika |
| collection | DOAJ |
| description | The Internet of Underwater Things (IoUT) is a network of countless connected devices that monitor vast, uncharted water territories. These gadgets consists of cameras designed to capture images beneath the water’s surface. and then distribute it among themselves and save them in the cloud. However, the substantial amount of data produced can hinder the devices’ performance due to limited computational power and battery life. To tackle this, Block Compressed Sampling (BCS) can be used to compress data, but it may result in distorted images after recovery. To tackle this problem, the Dynamic Block Compressive Sampling (DBCS) technique is utilized. This study introduces the Entropy-based Dynamic Block Compressive Sampling (EDBCS) algorithm to enhance the sampling accuracy and visual clarity of the recovered image. Through this approach, blocks with greater entropy receive increased measurements, while those with lower energy receive fewer ones. The suggested method has outperformed existing techniques, yielding superior results. |
| format | Article |
| id | doaj-art-84716d7bf47345c1913d66e5c857bac3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-84716d7bf47345c1913d66e5c857bac32025-08-20T03:40:51ZengIEEEIEEE Access2169-35362025-01-0113463954640710.1109/ACCESS.2025.354844310912481Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research PerspectiveR. Monika0https://orcid.org/0000-0002-7814-6611Samiappan Dhanalakshmi1https://orcid.org/0000-0002-6970-2719R. Narayanamoorthi2https://orcid.org/0000-0003-4842-3275Hossam Kotb3https://orcid.org/0000-0002-4052-6731Amr Yousef4https://orcid.org/0000-0003-0875-6462Department of ECE, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, IndiaDepartment of ECE, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, IndiaDepartment of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria, EgyptElectrical Engineering Department, University of Business and Technology, Jeddah, Saudi ArabiaThe Internet of Underwater Things (IoUT) is a network of countless connected devices that monitor vast, uncharted water territories. These gadgets consists of cameras designed to capture images beneath the water’s surface. and then distribute it among themselves and save them in the cloud. However, the substantial amount of data produced can hinder the devices’ performance due to limited computational power and battery life. To tackle this, Block Compressed Sampling (BCS) can be used to compress data, but it may result in distorted images after recovery. To tackle this problem, the Dynamic Block Compressive Sampling (DBCS) technique is utilized. This study introduces the Entropy-based Dynamic Block Compressive Sampling (EDBCS) algorithm to enhance the sampling accuracy and visual clarity of the recovered image. Through this approach, blocks with greater entropy receive increased measurements, while those with lower energy receive fewer ones. The suggested method has outperformed existing techniques, yielding superior results.https://ieeexplore.ieee.org/document/10912481/Internet of Underwater Thingsdynamic block compressive samplingentropy based DBCSimage reconstructionmeasurement matrix |
| spellingShingle | R. Monika Samiappan Dhanalakshmi R. Narayanamoorthi Hossam Kotb Amr Yousef Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective IEEE Access Internet of Underwater Things dynamic block compressive sampling entropy based DBCS image reconstruction measurement matrix |
| title | Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective |
| title_full | Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective |
| title_fullStr | Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective |
| title_full_unstemmed | Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective |
| title_short | Entropy-Driven Dynamic Block Compressive Sampling for Underwater Image Compression in the Context of IoUT: A Research Perspective |
| title_sort | entropy driven dynamic block compressive sampling for underwater image compression in the context of iout a research perspective |
| topic | Internet of Underwater Things dynamic block compressive sampling entropy based DBCS image reconstruction measurement matrix |
| url | https://ieeexplore.ieee.org/document/10912481/ |
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