Feedforward artificial neural networks (FANNs), which have been successfully
applied to various image processing tasks, are particularly suitable for image subsampling
due to their high processing speed. However, the performance of FANNs in
image subsampling, which depends on both the FANN topology and the FANN training
algorithm, has not been acceptable so far. High performance image subsampling
is important in many systems, such as subband decomposition systems, and scalable
image and video processing systems.
This thesis addresses the design of FANNs with application to image subsampling.
More specifically, we focus on both the topological design of FANNs and the
training algorithm, so that efficient FANN structures, yielding good performance in
image subsampling, are obtained. That is, we aim at obtaining compact FANNs that
yield good subsampled versions of the original images, such that if reconstructed,
they are as close as possible to the original images. Moreover, we aim at obtaining
better performance-speed tradeoffs than those of the traditional lowpass filtering and
subsampling methods.
First, we propose a design method for FANNs, which leads to compact tridiagonally
symmetrical feedforward neural networks (TS—FANNs). Next, in order to address the problem of artifacts that generally appear in the reconstructed images
after FANN-based subsampling, we propose a training method for FANNs. When
applied to first-order (FOS) and multi-stage first-order (MFOS) image subsampling,
the FANNs trained using our method outperform the traditional lowpass filtering
and subsampling (LPFS) method, without requiring pre- or post-processing stages.
Motivated by our observation that the computational demands of the MFOS process
increase approximately linearly with the image size, we then combine the proposed
methods and evaluate the performance-complexity tradeoffs of the resulting
TS-FANNs in FOS and MFOS. We show that our TS-FANNs-based subsampling
has important advantages over subsampling methods based on fully connected FANNs
(FC—FANNs) and LPFS, such as significantly reduced computational demands, and
the same, or better, quality of the resulting images.
The main contributions of this thesis consist of a method for FANN design
with tridiagonal symmetry constraints, a training algorithm for FANNs applied to
image subsampling, the design and evaluation of the performance-speed tradeoffs of
FC—FANNs in image subsampling, and the design and evaluation of the performancespeed
tradeoffs of TS—FANNs in image subsampling. The FANN performance in
image subsampling is evaluated objectively (using the peak signal-to-noise ratios),
subjectively (by visual examination of the subsampled and of the reconstructed images),
and in the context of a video coding application. The speed and memory
demands of the designed FANN structures are evaluated in terms of the subsampling
time and the number of FANN parameters, respectively. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/41445 |
| Date | January 1999 |
| Creators | Dumitras, Adriana |
| Publisher | University of British Columbia |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
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