Novel transform for image description and compression with implementation by neural architectures

Jezekiel Ben-Arie; Raghunath K. Rao

doi:10.1117/12.48394

1 October 1991 Novel transform for image description and compression with implementation by neural architectures

Jezekiel Ben-Arie, Raghunath K. Rao

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48394
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

A general method for signal representation using nonorthogonal basis functions that are composed of Gaussians are described. The Gaussians can be combined into groups with predetermined configuration that can approximate any desired basis function. The same configuration at different scales forms a set of self-similar wavelets. The general scheme is demonstrated by representing a natural signal employing an arbitrary basis function. The basic methodology is demonstrated by two novel schemes for efficient representation of 1-D and 2- D signals using Gaussian basis functions (BFs). Special methods are required here since the Gaussian functions are nonorthogonal. The first method employs a paradigm of maximum energy reduction interlaced with the A* heuristic search. The second method uses an adaptive lattice system to find the minimum-squared error of the BFs onto the signal, and a lateral-vertical suppression network to select the most efficient representation in terms of data compression.

Citation Download Citation

Jezekiel Ben-Arie and Raghunath K. Rao "Novel transform for image description and compression with implementation by neural architectures", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48394

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