## TR#188: On Training Gaussian Radial Basis Functions for Image Coding

### Alex Sherstinsky and Rosalind W. Picard

Article available in:
Revised Version appears in

IEEE Trans. Neural Nets

See Technical Report #271

The efficiency of the Orthogonal Least Squares (OLS) method for training
approximation networks is examined using the criterion of energy compaction.
We show that the selection of basis vectors produced by the procedure is not
the most compact when the approximation is performed using a non-orthogonal
basis. Hence, the algorithm does not produce the smallest possible networks
for a given approximation error. Specific examples are given using the
Gaussian Radial Basis Functions (RBF) type of approximation networks. A new
procedure that finds the most compact subset of non-orthogonal basis vectors
is described and used to evaluate the performance of OLS in image coding. The
new procedure also permits a comparison of the Gaussian RBFs to the Discrete
Cosine Transform (DCT), an orthogonal basis commonly used in image coding.
This comparison shows that in terms of efficiency, the Gaussian RBFs can
perform close to the DCT. Differences in perceptual distortion produced by
the two coding techniques are also discussed.

PDF .
Full list of tech reports