Transform coding by lateral inhibited neural nets
Rüdiger W. Brause · 2002
One of the most popular encoding techniques for sensor data is transform coding. This encoding schema is composed of two stages: a linear transformation stage with a nonzero kernel and a vector quantization stage. For the first stage, the author describes a new implementation approach by artifical neural networks. The problem of determining the optimal transformation coefficients is solved by learning the coefficients by a lateral inhibited neural network. After a short introduction to the topic the author focuses on this model and a local stability analysis of the fixpoints for the serial dynamics is provided. The resulting parameter regime is used in a network simulation example using picture statistics. Additionally, the simulations reveal that a biologically-like growing lateral inhibition influence leads to a speed-up of the learning convergence of that model.