Video quality assessment and machine learning: Performance and interpretability

Jacob Søgaard, Søren Forchhammer, Jari Korhonen

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

9 Citations (Scopus)

Abstract

In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice.

Original languageEnglish
Title of host publication2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479989584
DOIs
Publication statusPublished - 2 Jul 2015
Event2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015 - Costa Navarino, Messinia, Greece
Duration: 26 May 201529 May 2015

Conference

Conference2015 7th International Workshop on Quality of Multimedia Experience, QoMEX 2015
Country/TerritoryGreece
CityCosta Navarino, Messinia
Period26/05/1529/05/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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