Grayscale images and RGB video: compression by morphological neural network

Authors:
Osvaldo de Souza;Paulo César Cortez;Francisco A. T. F. da Silva
Affiliations:
DETI, Federal University of Ceará, Fortaleza, Brazil;DETI, Federal University of Ceará, Fortaleza, Brazil;National Institute For Space Research, ROEN, Eusébio, Brazil
Venue:
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Year:
2012

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Abstract

This paper investigates image and RGB video compression by a supervised morphological neural network. This network was originally designed to compress grayscale image and was then extended to RGB video. It supports two kinds of thresholds: a pixel-component threshold and pixel-error counting threshold. The activation function is based on an adaptive morphological neuron, which produces suitable compression rates even when working with three color channels simultaneously. Both intra-frame and inter-frame compression approaches are implemented. The PSNR level indicates that the compressed video is compliant with the desired quality levels. Our results are compared to those obtained with commonly used image and video compression methods. Network application results are presented for grayscale images and RGB video with a 352 × 288 pixel size.