Treffer: Fusion of Global and Local Side Information using Support Vector Machine in Transform-Domain Distributed Video Coding
Title:
Fusion of Global and Local Side Information using Support Vector Machine in Transform-Domain Distributed Video Coding
Contributors:
Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Holy-Spirit University of Kaslik [Jounieh], EURASIP
Source:
European Signal Processing Conference (EUSIPCO 2012), EURASIP, Aug 2012, Bucharest, Romania
Publisher Information:
HAL CCSD, 2012.
Publication Year:
2012
Collection:
collection:INSTITUT-TELECOM
collection:CNRS
collection:ENST
collection:TELECOM-PARISTECH
collection:PARISTECH
collection:LTCI
collection:IDS
collection:MM
collection:INSTITUTS-TELECOM
collection:INSTITUT-MINES-TELECOM
collection:CNRS
collection:ENST
collection:TELECOM-PARISTECH
collection:PARISTECH
collection:LTCI
collection:IDS
collection:MM
collection:INSTITUTS-TELECOM
collection:INSTITUT-MINES-TELECOM
Subject Terms:
Distributed Video Coding, Support Vector Machine, Classification, Side Information, Rate-Distortion Performance, [INFO.INFO-TI]Computer Science [cs], Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs], Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics], Signal and Image processing
Subject Geographic:
Original Identifier:
HAL: hal-01436379
Document Type:
Konferenz
conferenceObject<br />Conference papers
Language:
English
Access URL:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.01436379v1
Database:
HAL
Weitere Informationen
Side information has a strong impact on the performance of Distributed Video Coding. Commonly, side information is generated using motion compensated temporal interpolation. In this paper, we propose a new method for the fusion of global and local side information using Support Vector Machine. The global side information is generated at the decoder using global motion parameters estimated at the encoder using the Scale-Invariant Feature Transform. Experimental results show that the proposed approach can achieve a PSNR improvement of up to 1.7 dB for a GOP size of 2 and up to 3.78 dB for larger GOP sizes, with respect to the reference DISCOVER codec.