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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 bit stream model for video quality assessment of H.264/AVC video based on packet loss visibility
Zitatschlüssel argyropoulos2011b
Autor Argyropoulos, Savvas and Raake, Alexander and Garcia, Marie-Neige and List, Peter
Buchtitel IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP)
Seiten 1169–1172
Jahr 2011
ISBN 978-1-4577-0537-3
ISSN 1520-6149
Ort Prague, Czech Republic
Adresse Prague, Czech Republic
Monat may
Notiz electronic/online
Verlag IEEE
Wie herausgegeben poster
Zusammenfassung In this paper, a no reference bit stream model for quality assessment of SD and HD H.264/AVC video sequences based on packet loss visibility is proposed. The method considers the impact of network impairments on human perception and uses the visibility of packet losses to predict objective scores. Also, a new subjective experiment has been designed to provide insight into the perceptual effect of degradations caused by transmission errors. The proposed algorithm extracts a set of features from the received bit stream. Then, the visibility of each packet loss event is determined by classifying the extracted features using a Support Vector Machines classifier. Finally, analytical expressions are developed to account for visual degradation due to compression and channel induced distortion based on the outcome of the visibility classifier. The evaluation demonstrates the validity of the proposed method.
Link zur Originalpublikation Download Bibtex Eintrag

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