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Forward-looking sonar image compression by integrating keypoint clustering and morphological skeleton

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Abstract

Forward-Looking Sonar (FLS) is one of the most effective devices for underwater exploration which provides high-resolution images that can be used for several tasks in marine research, oceanographic, and deep-sea exploration. The limitation of current underwater acoustic channels does not allow transmitting these images in real-time, therefore image compression is required. Since acoustic images are characterized by speckle noise, an important challenge, in this area, is how to perform the compression while preserving relevant information. In this paper, a novel lossy forward-looking acoustic image compression method based on the combination between keypoint clustering and Morphological Skeleton (MS) is proposed. Keypoints are extracted by using A-KAZE feature extractor, while Density-Based Spatial Clustering of Application with Noise (DBSCAN) is used to find keypoint clusters representing a region-of-interest (ROI). Then, MS is executed to compact the ROI. The rest of the image is down-sampled and quantized through K-Means Clustering and represented via colour indexing. Finally, the information is compressed by using Brotli data compression. The experimental results on real FLS images demonstrate that our method achieves good outcomes in terms of quality metrics and compression ratio.

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  1. The videos are available on: http://www.teledynemarine.com/ProViewer

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Acknowledgments

This work was supported in part by the MIUR under grant “Departments of Excellence 2018-2022” of the Department of Computer Science of Sapienza University and by the European Union within the Project ARCHEOSUb “Autonomous underwater Robotic and sensing systems for Cultural Heritage discovery Conservation and in situ valorization”. The authors wish to thank the Teledyne Marine group (part of the Teledyne Technologies Incorporated) to have provided us with sample data for experimental purposes.

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Authors and Affiliations

  1. Department of Computer Science, Sapienza University, Via Salaria 113, 00198, Rome, Italy

    Danilo Avola, Marco Bernardi, Luigi Cinque, Daniele Pannone & Chiara Petrioli

  2. Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, 33100, Udine, Italy

    Gian Luca Foresti

  3. Department of Communication and Social Research, Sapienza University, Via Salaria 113, 00198, Rome, Italy

    Danilo Avola

  4. WSENSE S.r.l, Via Gaetano Donizetti 4, 00198, Rome, Italy

    Danilo Avola, Marco Bernardi, Daniele Pannone & Chiara Petrioli

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  1. Danilo Avola
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  2. Marco Bernardi
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  3. Luigi Cinque
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  4. Gian Luca Foresti
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  5. Daniele Pannone
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  6. Chiara Petrioli
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Correspondence to Danilo Avola.

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Avola, D., Bernardi, M., Cinque, L. et al. Forward-looking sonar image compression by integrating keypoint clustering and morphological skeleton. Multimed Tools Appl 80, 1625–1639 (2021). https://doi.org/10.1007/s11042-020-09670-3

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  • DOI: https://doi.org/10.1007/s11042-020-09670-3

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