| Abstract |
This paper introduces evolutionary computing to fractal
image compression. In fractal image compression
[1] a partitioning of the image into ranges is required.
We propose to use evolutionary computing to find good
partitionings. Here ranges are connected sets of small
square image blocks. Populations consist of Np configurations,
each of which is a partitioning with a fractal
code. In the evolution each configuration produces sigma
children who inherit their parent partitionings except
for two random neighboring ranges which are merged.
From the offspring the best ones are selected for the
next generation population based on a fitness criterion
(collage error). We show that a far better ratedistortion
curve can be obtained with this approach as
compared to traditional quad-tree partitionings. |