Volume: 17, Issue: 5(2007)
pp. 1703-1711 DOI: 10.1142/S0218127407018002
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Full Text (PDF, 396KB)
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| Title: |
CELLULAR NEURAL NETWORKS FOR VIDEO COMPRESSION: AN OBJECT-ORIENTED APPROACH
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| Author(s): |
GIUSEPPE GRASSI
Author for correspondence.
Dipartimento di Ingegneria dell'Innovazione, Università di Lecce, 73100 Lecce, ItalyPIETRO VECCHIO
Dipartimento di Ingegneria dell'Innovazione, Università di Lecce, 73100 Lecce, ItalyLUIGI A. GRIECO
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, 70124 Bari, ItalyEUGENIO DI SCIASCIO
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, 70124 Bari, Italy
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| History: |
Received March 14, 2006
Revised April 19, 2006
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| Abstract: |
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. |
| Keywords: |
CNN; video compression; image analysis; object-oriented methods
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