Paper Info
Title
Forward-Looking Sonar Image Compression By Integrating Keypoint Clustering And Morphological Skeleton
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.
Year
DOI
Venue
2021
10.1007/s11042-020-09670-3
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Forward-Looking sonar, Image compression, Keypoint clustering, Morphological skeleton, Color indexing
Journal
80
Issue
ISSN
Citations 
2
1380-7501
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Danilo Avola113425.14
Marco Bernardi263.28
Luigi Cinque339841.17
Gian Luca Foresti4447.06
Daniele Pannone584.57
C. Petrioli61713157.55