Here are a couple of bullets on SVD and its impact on image compression:

• Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three constituent matrices. For images, it can be used to extract the dominant patterns of variation, known as "eigenimages."

• The eigenimages from SVD can be used for image compression by keeping only the most significant eigenimages and projecting the original image onto this reduced space. This allows for significant compression while preserving much of the important aspects of the image. SVD-based compression has been influential in image and video compression research and applications.
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