Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver

This project demonstrates the application of Singular Value Decomposition (SVD) for image compression using Python and NumPy.

Image and video compression via singular value decomposition with user-interface.

A jupyter notebook showing how images are compressed using Singular Value Decomposition in Python

Image compression using singular value decomposition method (SVD) in Matlab.

Linear Algebra projects of Spring 2021 at CE AUT

Implementations and analyses of various mathematical and computational techniques, including Lagrange interpolation, LU decomposition, image compression with SVD and FFT, image denoising, histogram matching, and QR decomposition using Gram-Schmidt methods.

Explanations about three projects of Linear Algebra course; including LU decompositions, denoising signals, and SVD decompositions

Flask web app to compress images by SVD method. Tugas Besar 2 IF2123 Aljabar Linier dan Geometri

[Rmd/HTML] Implementation of the Randomized Power Method to compute the SVD + Image compression with SVD

AUT Applied Linear Algebra course projects

Image compression using SVD in Python using NumPy, Pillow and Matplotlib.

SVD is basically a matrix factorization technique, which decomposes any matrix into 3 generic and familiar matrices. It has some cool applications in Machine Learning and Image Processing.