Back to articles
Articles
Volume: 33 | Article ID: art00019
Image
Analyzing the effect of adding temporal features to an autoencoder-based video quality model
  DOI :  10.2352/ISSN.2470-1173.2021.9.IQSP-261  Published OnlineJanuary 2021
Abstract

According to Cisco, most Internet traffic is currently comprised of videos. Therefore, developing a quality assessment method for assuring that those videos are received and displayed with quality at the user side is an important and challenging task. As a consequence, over the last decades, several no-reference video quality metrics have been proposed with the goal of blindly predicting (with no access to the original signal) the quality of videos in streaming applications. One of such metrics is NAVE, whose architecture includes an auto-encoder module that produces a compact set of visual features with a higher descriptive capacity. Nevertheless, the visual features in NAVE do not include descriptive temporal features that are sensitive to temporal degradation. In this work, we analyze the effect on accuracy performance of using a new type of temporal features, based on natural scene statistics. This approach has the goal of making the tested video quality metric more generic, i.e. sensitive to both spatial and temporal distortions and therefore adequate for video streaming applications.

Subject Areas :
Views 32
Downloads 2
 articleview.views 32
 articleview.downloads 2
  Cite this article 

André H. M. Costa, Helard Becerra Martinez, Daniel G. Silva, Mylène C.Q. Farias, "Analyzing the effect of adding temporal features to an autoencoder-based video quality modelin Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVIII,  2021,  pp 261-1 - 261-8,  https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-261

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA