In this paper we propose to exploit cellular neural networks (CNNs) as a computational tool to obtain real-time compression of video sequences. In particular, we present a CNN-based architecture, which combines object-oriented CNN algorithms and basic coding/decoding MPEG capabilities. The proposed real-time compression architecture has been tested using standard benchmarking video sequences. Simulation results, in terms of compression ratio and peak to signal noise ratio, show that the proposed approach enables CNN-based real-time coding systems with satisfying compression ratios and good visual appearance.

A CNN-based object-oriented coding system for real-time video compression

Di Sciascio, E.;GRIECO, Luigi Alfredo;
2004-01-01

Abstract

In this paper we propose to exploit cellular neural networks (CNNs) as a computational tool to obtain real-time compression of video sequences. In particular, we present a CNN-based architecture, which combines object-oriented CNN algorithms and basic coding/decoding MPEG capabilities. The proposed real-time compression architecture has been tested using standard benchmarking video sequences. Simulation results, in terms of compression ratio and peak to signal noise ratio, show that the proposed approach enables CNN-based real-time coding systems with satisfying compression ratios and good visual appearance.
2004
IEEE 6th Workshop on Multimedia Signal Processing, MMSP 2004
0-7803-8578-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/21018
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