Using statistical analysis and artificial intelligence tools for automatic assessment of video sequences

Brice Ekobo Akoa; Emmanuel Simeu; Fritz Lebowsky

doi:10.1117/12.2044797

3 February 2014 Using statistical analysis and artificial intelligence tools for automatic assessment of video sequences

Brice Ekobo Akoa, Emmanuel Simeu, Fritz Lebowsky

Author Affiliations +

Proceedings Volume 9015, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications; 90150O (2014) https://doi.org/10.1117/12.2044797
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

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.

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Brice Ekobo Akoa, Emmanuel Simeu, and Fritz Lebowsky "Using statistical analysis and artificial intelligence tools for automatic assessment of video sequences", Proc. SPIE 9015, Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, 90150O (3 February 2014); https://doi.org/10.1117/12.2044797

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