Digital Image processing
lectures and exercises
Lecture topics by Paul Cockshott
Transforms and frequency domain filters
Notes on information
theory
These go into the topic in more depth than the
lecture
A Sample
examination paper with answers is available HERE
Exercises
2. Use of
Kmeans algorithm. This is a Marked
Exercise
Write a Matlab mfunction that can
be used to vector quantize an image. The image to be quantized should be
greyscale.
The format of the function should
be
[A,C]=vq(f,patchsize,
alphabetsize)
Where
A is an encoded image
C is the codebook used to encode
the image. The codebook should be a 3 d array with bounds
1..alphabetsize,1..patchsize,1..patchsize
f is a source image
patchsize is the length in
pixels of the square to be used in encoding and decoding
alphabetsize is the number of
distinct patches in the alphabet that is used to encode or decode the picture.
A second function
F=vdq(A,C)
Should be written which will
decode the encoded image A, using codebook C and return an image F.
You should test the code using the
The tests should use 2x2 patches,
and 4x4 patches and the alphabet sizes used should be 32 , 512.
This will yield 8 resultant test images. You should submit a report containing
1)
Your code
2)
The source images
3)
The results of encoding and decoding the images, these
should be formatted on a page to allow easy comparison.
4)
A table showing the frequency of use of the different
codes in the alphabets for each of the two images.
5)
An estimate of the number of bits used to encode the
images. This estimate should list separately the bits used to encode the arrays
A and C that output from the encoder.
The report should be produced
either as a .pdf document or a word document. It is worth 10 marks.
It should be handed in at the end
of week 7.
Images
Fractp.jpg
Grey1
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