Application of singular value decomposition in image compression

Abstract

The field of computer graphics and multimedia technologies places significant emphasis on the study path of image compression. The increasing prevalence of digital photographs has led to a significant growth in the size of image files, hence presenting storage and transmission issues. The Singular Value Decomposition (SVD) is a matrix decomposition method that possesses significant computational capacity and finds utility in various domains. In recent years, SVD has emerged as a prevalent technique in the domain of image processing, particularly in the context of image compression, yielding notable outcomes. This work primarily employs a literature review research methodology to explore the application of SVD. Specifically, SVD is applied to image compression using Matlab. The approach holds significance due to its ability to achieve efficient compression of digital images, hence enhancing transmission efficiency without compromising image quality.