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A No-Reference Autoencoder Video Quality Metric

Citation Author(s):
Helard B. Martinez ; Mylène C. Q. Farias ; Andrew Hines
Submitted by:
Mylene Farias
Last updated:
22 September 2019 - 12:53pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters Name:
Mylene Farias
Paper Code:
2637
Categories:

Abstract 

Abstract: 

In this work, we introduce the No-reference Autoencoder VidEo (NAVE) quality metric, which is based on a deep au-toencoder machine learning technique. The metric uses a set of spatial and temporal features to estimate the overall visual quality, taking advantage of the autoencoder ability to produce a better and more compact set of features. NAVE was tested on two databases: the UnB-AVQ database and the LiveNetflix-II database. Results show that the method is able to estimate the perceived video quality with a good correlation performance and a small error, when compared to currently available no-reference and full-reference video quality objective metrics.

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2019-09-ICIP-presentation.pdf

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