In linear algebra, the Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix into three matrices. It has some interesting algebraic properties and conveys important geometrical and theoretical insights about linear transformations. It also has some important applications in data science. In this article, I will try to explain the mathematical intuition behind SVD and its geometrical meaning. Instead of manual calculations,
i will be using the SVD method by using the function [U S V] =svd (gray_image,'econ') i have done this compression in the white black mode but it was a buttery work for me thanks to the functions present in the matlab
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AMIT SURYAVANSHI (2024). IMAGE COMPRESSION USING SVD [SINGULAR VALUE DECOMPOSITION] (https://www.mathworks.com/matlabcentral/fileexchange/157651-image-compression-using-svd-singular-value-decomposition), MATLAB Central File Exchange. 检索来源 .
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