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Marie-Neige Garcia


Marie-Neige Garcia is now with the Fachgebiet Assessment of IP-based Applications.

Research Field:
- Quality and Usability Research Topics: - Video and Audio-Visual Quality Assessment
- Quality modeling
Biography Since 2006: PhD Student at Technische Universität Berlin / Deutsche Telekom Laboratories 2004-2006: Project manager at ELDA (Evaluation and Language Resources Distribution Agency) , Speech evaluation department - Paris, France

  • Project manager in the EVALDA/EvaSy campaign (Evaluation of speech synthesis systems in French)
  • In charge of the evaluation of Text-to-speech systems in the European project TC-STAR (speech-to-speech translation)
  • In charge of the evaluation of video technologies in the project CHIL (Pervasive Interaction Computing)

2003: Master’s Degree in Engineering at ISEP (Electronics Superior Institute of Paris); areas of study: informatics, telecommunications, electronics
Address Quality and Usability Lab
Deutsche Telekom Laboratories
TU Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin, Germany



No-reference video quality assessment of SD and HD H.264/AVC sequences based on continuous estimates of packet loss visibility
Zitatschlüssel argyropoulos2011c
Autor Argyropoulos, Savvas and Raake, Alexander and Garcia, Marie-Neige and List, Peter
Buchtitel IEEE Int. Conf. on Quality of Multimedia Experience
Seiten 31–36
Jahr 2011
ISBN 978-1-4577-1334-7
DOI 10.1109/QoMEX.2011.6065708
Adresse Mechelen, Belgium
Monat sep
Notiz electronic/online
Verlag IEEE
Wie herausgegeben full
Zusammenfassung In this paper, a novel method for predicting the visibility of packet losses in SD and HD H.264/AVC video sequences and modeling their impact on perceived quality is proposed. Based on the findings of a new subjective experiment it is initially shown that the classification of packet loss visibility in a binary fashion is not sufficient to model the perceptual degradations caused by the transmission errors. The proposed no-reference algorithm extracts a set of features from the video bit-stream to account for the spatial and temporal characteristics of the video content and the induced distortion due to the network impairments. Subsequently, the visibility of packet losses is predicted in a continuous fashion using Support Vector Regression. Finally, a no-reference bit-stream based video quality assessment model that explicitly employs the predicted packet loss visibility estimates is presented. The evaluation of the proposed model demonstrates that the use of continuous estimates for the visibility of packet losses improves the performance of the video quality assessment model.
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