Naive SVD-based image compression

gistfile1.py

from pylab import *

def makeApprox(channel,n=20):
    u,eigVals,vt = svd(channel)
    approx = zeros_like(channel)
#     for i in xrange(n):
#         approx += outer(u[:,i],vt[i,:])*eigVales[i]
    approx = dot(u[:,:n]*eigVals[:n], vt[:n,:])
    ## Fractional error in matrix approximation
    error = sqrt(sum(eigVals[n+1:]**2)/sum(eigVals**2))
    return approx,error    

pat = imread('pat.png')

k = 50

r,rerr = makeApprox(pat[:,:,0],k)
g,gerr = makeApprox(pat[:,:,1],k)
b,berr = makeApprox(pat[:,:,2],k)

imshow(dstack((r,g,b)))
show()