|dc.description.abstract||Fractal image compression exploits the piecewise self-similarity present in real images
as a form of information redundancy that can be eliminated to achieve compression. This
theory based on Partitioned Iterated Function Systems is presented. As an alternative to the
established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal
techniques promise faster decoding and potentially higher fidelity, but the computationally
intensive compression process has prevented commercial acceptance.
This thesis presents an algorithm mapping the problem onto a parallel processor
architecture, with the goal of reducing the encoding time. The experimental work involved
implementation of this approach on the Texas Instruments TMS320C80 parallel processor
system. Results indicate that the fractal compression process is unusually well suited to
parallelism with speed gains approximately linearly related to the number of processors used.
Parallel processing issues such as coherency, management and interfacing are discussed. The
code designed incorporates pipelining and parallelism on all conceptual and practical levels
ensuring that all resources are fully utilised, achieving close to optimal efficiency.
The computational intensity was reduced by several means, including conventional
classification of image sub-blocks by content with comparisons across class boundaries
prohibited. A faster approach adopted was to perform estimate comparisons between blocks
based on pixel value variance, identifying candidates for more time-consuming, accurate
RMS inter-block comparisons. These techniques, combined with the parallelism, allow
compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB
PSNR. This is up to an order of magnitude faster than reported for conventional sequential
processor implementations. Fractal based compression of colour images and video sequences
is also considered.
The work confirms the potential of fractal compression techniques, and demonstrates
that a parallel implementation is appropriate for addressing the compression time problem.
The processor system used in these investigations is faster than currently available PC
platforms, but the relevance lies in the anticipation that future generations of affordable
processors will exceed its performance. The advantages of fractal image compression may
then be accessible to the average computer user, leading to commercial acceptance.||en