Underwater image compression using energy based adaptive block compressive sensing for IoUT applications

Abstract

Internet of Underwater Things (IoUT) consists of a large number of interconnected resource-constrained underwater devices that are capable of monitoring vast unexplored water bodies. Specifically, these devices are equipped with cameras to capture the underwater scenes and communicate them with each other and also with the cloud. However the data generated is very high which limits the performance of the IoUT devices in terms of computational capabilities and battery lifetime. Block Compressed Sensing technique which performs block by block fixed sampling can be utilized to achieve data compression however it ends up in image distortions after reconstruction. To unravel this issue, Adaptive Block Compressive Sensing technique is used. In this paper, Energy based Adaptive Block Compressive Sensing (EABCS) with Orthogonal Matching Pursuit reconstruction algorithm is proposed to improve the sampling performance and visual quality of the reconstructed image. Sparse binary random matrix is used as measurement matrix as it is highly sparse. With this energy based adaptive strategy, higher measurements are assigned to blocks with higher energy and vice versa. The proposed EABCS technique has achieved better compression with approximately 25–30% of measurements/samples with an increase in Peak signal to noise ratio of about 3–5 dB and structural similarity Index of around 0.1–0.3 with respect to other adaptive strategies. Percentage of space saving is also about 60–70%.