<p>In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (CS), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compression, leveraging compressed sensing techniques to achieve optimized results. Our method works by expressing image data in a higher-order non-raw and non-dense format hence dramatically decreasing the size of the entire data. This significantly reduces storage requirements while preserving image quality. By combining advanced signal processing with innovative image analysis techniques, our approach maintains low computational complexity, enabling real-time applications without sacrificing the fidelity of reconstructed images, even after compression. This makes our approach especially valuable for applications where storage space is limited but image integrity remains crucial. We present experimental results demonstrating the effectiveness of our technique across various image types and compression ratios. Our findings underscore the potential of compressed sensing as a transformative paradigm in the realm of image compression, paving the way for more efficient storage and transmission of visual content in diverse applications.</p>