Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

2047

Comments

This conference paper was originally published as: Bouzerdoum, A. (2001). Image compression using a stochastic competitive learning algorithm (scola). Proceedings of 6th Internatrional Symposium on Signal Processing and its Applications. (pp. 541-544). Malaysia. IEEE. Original article available here

© 2001 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Abstract

We introduce a new stochastic competitive learning algorithm (SCoLA) and apply it to vector quantization for image compression. In competitive learning, the training process involves presenting, simultaneously, an input vector to each of the competing neurons, which then compare the input vector to their own weight vectors and one of them is declared the winner based on some deterministic distortion measure. Here a stochastic criterion is used for selecting the winning neuron, whose weights are then updated to become more like the input vector. The performance of the new algorithm is compared to that of frequency-sensitive competitive learning (FSCL); it was found that SCoLA achieves higher peak signal-to-noise ratios (PSNR) than FSCL

DOI

10.1109/ISSPA.2001.950200

Access Rights

free_to_read

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Link to publisher version (DOI)

10.1109/ISSPA.2001.950200