Image Compression Algorithm Using Compressive Sensing with Orthogonalized Gaussian Measurement Matrix Based on OMP Reconstruction

This research presents an image compression algorithm based on compressive sensing using an orthogonalized Gaussian measurement matrix reconstructed through Orthogonal Matching Pursuit (OMP). The algorithm employs OMP reconstruction methodology to generate an orthogonalized Gaussian measurement matrix that facilitates efficient image compression. Unlike traditional compression techniques, this approach leverages compressive sensing principles to preserve critical image information during the compression process, achieving higher compression ratios while maintaining image quality. Key implementation aspects include: - OMP algorithm implementation for sparse signal reconstruction with iterative orthogonal projections -Creation of orthogonalized Gaussian measurement matrices through Gram-Schmidt orthogonalization process - Sparse representation of image data in appropriate transform domains (e.g., DCT, wavelet) - Optimization of measurement matrix properties to satisfy Restricted Isometry Property (RIP) conditions The algorithm demonstrates significant potential for applications in image processing, video compression, and data transmission systems, particularly where bandwidth constraints and reconstruction quality are critical factors. Code implementation typically involves matrix operations for measurement generation, sparse recovery algorithms for reconstruction, and optimization routines for parameter tuning.