Color separations and the SVD

This demonstration shows how to work with color channels to explore image compression using the Singular Value Decomposition (SVD).

using Images, TestImages, LinearAlgebra

img = testimage("mandrill")
channels = channelview(float.(img))

function rank_approx(F::SVD, k)
    U, S, V = F
    M = U[:, 1:k] * Diagonal(S[1:k]) * V[:, 1:k]'
    M = min.(max.(M, 0.0), 1.)
# after julia v1.1: svd.(eachslice(channels; dims=1))
svdfactors = (svd(channels[1,:,:]), svd(channels[2,:,:]), svd(channels[3,:,:]))
imgs = map((10, 50, 100)) do k
              rank_approx(svdfactors[1], k),
              rank_approx(svdfactors[2], k),
              rank_approx(svdfactors[3], k))

vcat([img imgs[1]], [imgs[2] imgs[3]])

This page was generated using DemoCards.jl and Literate.jl.