In linear algebra, the Singular Value Decomposition (SVD) of a matrix is a factorization of that matrix(A) into three matrices (U,S and Vᵀ: U and V are orthogonal matrices with orthonormal eigenvectors chosen from AAᵀ and AᵀA respectively. S is a diagonal matrix with r elements equal to the root of the positive eigenvalues of AAᵀ or Aᵀ A (both matrics have the same positive eigenvalues). The diagonal elements are composed of singular values).
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. Here, we use Singular Value Decomposition (SVD) to compress an image. scikit-image is a collection of algorithms for image processing.