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Proceedings Paper

Using statistical analysis and artificial intelligence tools for automatic assessment of video sequences
Author(s): Brice Ekobo Akoa; Emmanuel Simeu; Fritz Lebowsky
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Paper Abstract

This paper proposes two novel approaches to Video Quality Assessment (VQA). Both approaches attempt to develop video evaluation techniques capable of replacing human judgment when rating video quality in subjective experiments. The underlying study consists of selecting fundamental quality metrics based on Human Visual System (HVS) models and using artificial intelligence solutions as well as advanced statistical analysis. This new combination enables suitable video quality ratings while taking as input multiple quality metrics. The first method uses a neural network based machine learning process. The second method consists in evaluating the video quality assessment using non-linear regression model. The efficiency of the proposed methods is demonstrated by comparing their results with those of existing work done on synthetic video artifacts. The results obtained by each method are compared with scores from a database resulting from subjective experiments.

Paper Details

Date Published: 3 February 2014
PDF: 11 pages
Proc. SPIE 9015, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, 90150O (3 February 2014); doi: 10.1117/12.2044797
Show Author Affiliations
Brice Ekobo Akoa, TIMA Lab. (France)
Emmanuel Simeu, TIMA Lab. (France)
Fritz Lebowsky, STMicroelectronics (France)


Published in SPIE Proceedings Vol. 9015:
Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications
Reiner Eschbach; Gabriel G. Marcu; Alessandro Rizzi, Editor(s)

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