Color separations and the SVD

Color separations and the SVD

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

using Images, TestImages, LinearAlgebra, Interact

img = testimage("mandrill")
channels = float(channelview(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.)
svdfactors = (svd(channels[1,:,:]), svd(channels[2,:,:]), svd(channels[3,:,:]))

n = 100
@manipulate for k1 in 1:n, k2 in 1:n, k3 in 1:n
              rank_approx(svdfactors[1], k1),
              rank_approx(svdfactors[2], k2),
              rank_approx(svdfactors[3], k3)

Here's the result in IJulia:


You can click on the slider bars to change the number of components used in each color channel.