M
Mylène C. Q. Farias
Researcher at University of Brasília
Publications - 138
Citations - 1532
Mylène C. Q. Farias is an academic researcher from University of Brasília. The author has contributed to research in topics: Video quality & Computer science. The author has an hindex of 16, co-authored 112 publications receiving 1089 citations. Previous affiliations of Mylène C. Q. Farias include Intel & Federal University of São Paulo.
Papers
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Proceedings ArticleDOI
No-reference video quality metric based on artifact measurements
TL;DR: This paper presents a no-reference video quality metric based on individual measurements of three artifacts: blockiness, blurriness, and noisiness, developed using a proposed procedure that uses synthetic artifacts and subjective data obtained from previous experiments.
Journal ArticleDOI
A robust error concealment technique using data hiding for image and video transmission over lossy channels
TL;DR: Simulation results show that the proposed concealment technique using data hiding outperforms existing approaches in improving the perceptual quality, especially in the case of higher loss probabilities.
Journal ArticleDOI
Study of Subjective and Objective Quality Assessment of Audio-Visual Signals
TL;DR: The new LIVE-SJTU Audio and Video Quality Assessment (A/V-QA) Database includes 336 A/V sequences that were generated from 14 original source contents by applying 24 different A-V distortion combinations on them, and is validated and tested all of the objective A/v quality prediction models.
Journal ArticleDOI
Detection of Gabor patterns of different sizes, shapes, phases and eccentricities.
TL;DR: An experiment that shows that mixing sizes within the trial sequence has no effect on thresholds, suggests that the limiting noise does not increase with the number of mechanisms monitored, and an internal-noise-limited model.
Journal ArticleDOI
On performance of image quality metrics enhanced with visual attention computational models
TL;DR: Results show that the performance of simple quality metrics can be improved by adding visual attention information, but gains in performance depend on the precision of the visual attention model, the type of distortion, and the characteristics of the quality metric.