Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast adaptive digital equalization by recurrent neural networks
IEEE Transactions on Signal Processing
On-line learning algorithms for locally recurrent neural networks
IEEE Transactions on Neural Networks
Multilayer feedforward networks with adaptive spline activation function
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper a novel connectionist approach for video compression is presented. The basic idea is to extend to the temporal dimension the architecture used for the compression of still images. A multilayer perceptron (MLP) with infinite impulse response (IIR) synapses, embedded in a new quad-tree framework for video segmentation, is employed to take into account the video temporal dynamics. In order to reduce the computational burden and to improve the generalization performance, a flexible spline-based activation function, suitable for signal processing applications, has been used. Preliminary experimental results show that the proposed approach represents a viable alternative with respect to existing standards for high-quality video compression.