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. 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.