Video compression technologies have recently become an integral part of the way we create, communicate and consume visual information. The aim of this Letter is to show that the Cellular Neural Network (CNN) paradigm can be exploited for obtaining accurate video compression. In particular, the Letter presents an architecture that combines CNN algorithms and H.264 codec. The compression capabilities of the devised coding system are analyzed in detail using some benchmark video sequences, and comparisons are carried out between the CNN-based approach and the H.264 codec working alone. The outcome of the analysis is that the CNN-based coding approach outperforms the H.264 codec working alone, allowing to perceive the capabilities of the CNN paradigm.
|Autori interni:||GRIECO, Luigi Alfredo|
DI SCIASCIO, Eugenio
|Titolo:||Cellular Neural Networks for Video Compression: an Object-oriented Approach|
|Rivista:||INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS IN APPLIED SCIENCES AND ENGINEERING|
|Data di pubblicazione:||2007|
|Digital Object Identifier (DOI):||10.1142/S0218127407018002|
|Appare nelle tipologie:||1.1 Articolo in rivista|