Optimizing Hybrid Sparse and Block-Based Compressed Sensing: A Performance Study for Biomedical Imaging and Video Surveillance Applications

One of the most promising fields, compressed sensing has found a home in video streaming applications, where it seeks to compress or sparsely represent signals by reducing their dimensions. The sparse representation is a good approximation to the signal since the amplitude of the coefficient’s decays according to the power law. Signals are projected into low-dimensional space to generate measurement vectors. A sparse signal may be accurately rebuilt using the measurement vector. Signals or noise that are not sparse but can be compressed and might result in an approximation. Images in the frequency or wavelet domain are ideal for Compressed Sensing because they are compressible by nature. A more precise reconstruction of the original picture is possible due to an improvement in reconstructed image quality and a reduction in the compression ratio, which is the ratio of the dimensions of the number of pixels to the measurement vector.Hybrid Sparse and Block-based Compressed Sensing Algorithm (HSBCA) is the name of the approach that merged sparse-based and block-based compressed sensing algorithms. Block Compressed Sensing, the first module of HSSBCA, processes each message by dividing it into numerous picture regions, which results in decreased run time. In contrast, performance drops while picture reconstruction is underway; to remedy this, the research employs the second module, the sparse compressed sensing method, to boost reconstruction efficiency.