Share Email Print

Proceedings Paper

On linear transform design with non-linear approximation
Author(s): Osman G. Sezer; Onur G. Guleryuz
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this paper we share our recent observations on methods for sparsity enforced orthogonal transform design. In our previous work on this problem, our target was to design transforms (sparse orthonormal transforms - SOT) that minimize the overall sparsity-distortion cost of a collection of image patches mainly for improving the performance of compression methods. In this paper we go one step further to understand why these transforms achieve better approximation and how different they are from transforms like the DCT or the Karhunen-Loeve transform (KLT). Our study lead us to mathematically validate that for a Gaussian process the KLT is the optimal transform not only in a linear approximation sense but also in a nonlinear approximation sense, the latter forming the basis for sparsity-based regularization. This means that the search for SOTs yields the KLT in Gaussian processes, but results in transforms that are distinctly different from the KLT in non-Gaussian cases by capturing useful structures within the data. Both toy examples and real compression results in various representation domains are presented in this paper to support our observations.

Paper Details

Date Published: 26 September 2013
PDF: 14 pages
Proc. SPIE 8858, Wavelets and Sparsity XV, 88580Q (26 September 2013); doi: 10.1117/12.2026578
Show Author Affiliations
Osman G. Sezer, Texas Instruments Inc. (United States)
Onur G. Guleryuz, LG Electronics Mobile Research Lab. (United States)

Published in SPIE Proceedings Vol. 8858:
Wavelets and Sparsity XV
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?