Ever wondered how to compress images without losing quality?
Singular value decomposition
Ever wondered how to compress images without losing quality?
Imagine you want to reduce the size of a photo for a faster web page load while keeping it clear.
Think of the photo as a complex puzzle. SVD breaks it down into simpler pieces (rotations and scalings), rearranges them (U and V matrices), and then combines them back (Σ matrix) to recreate a clearer, smaller version.
Example
If a photo is represented by a 300x300 matrix, SVD helps us find simpler matrices that still preserve the main features of the photo when we multiply them together.
Remember this
SVD compresses images by finding simpler representations that maintain essential details, making web pages load faster without noticeable loss in quality.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
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