Neural Approach for TV Image Compression Using a Hopfield Type Network
Abstract
A self-organizing Hopfield network has been developed in the context of Vector Ouantiza(cid:173) -tion, aiming at compression of television images. The metastable states of the spin glass-like network are used as an extra the Minimal Overlap storage resource using and Mezard 1987) to rule (Krauth learning the organization of the attractors. optimize The sel f-organi zi ng that we have scheme devised the generation of an in adaptive codebook for any qiven TV image.
Cite
Text
Naillon and Theeten. "Neural Approach for TV Image Compression Using a Hopfield Type Network." Neural Information Processing Systems, 1988.Markdown
[Naillon and Theeten. "Neural Approach for TV Image Compression Using a Hopfield Type Network." Neural Information Processing Systems, 1988.](https://mlanthology.org/neurips/1988/naillon1988neurips-neural/)BibTeX
@inproceedings{naillon1988neurips-neural,
title = {{Neural Approach for TV Image Compression Using a Hopfield Type Network}},
author = {Naillon, Martine and Theeten, Jean-Bernard},
booktitle = {Neural Information Processing Systems},
year = {1988},
pages = {264-271},
url = {https://mlanthology.org/neurips/1988/naillon1988neurips-neural/}
}