Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning

• URL: http://arxiv.org/abs/2003.08730v1
• Date: Thu, 12 Mar 2020 15:28:10 GMT
• Title: Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning
• Authors: Selim Ickin and Markus Fiedler and Konstantinos Vandikas
• Abstract summary: Small datasets, lack of diversity in user profiles in source domain, and too much diversity in target domains of QoE models are challenges for QoE models. We present a transfer learning-based ML model training approach, which allows decentralized local models to share generic indicators on Mean Opinion Scores (MOS) We show that the proposed approach is agnostic to specific ML algorithms, stacked upon each other, as it does not necessitate the collaborating localized nodes to run the same ML algorithm.
• Score: 1.452875650827562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of QoE models by means of Machine Learning (ML) is challenging, amongst others due to small-size datasets, lack of diversity in user profiles in the source domain, and too much diversity in the target domains of QoE models. Furthermore, datasets can be hard to share between research entities, as the machine learning models and the collected user data from the user studies may be IPR- or GDPR-sensitive. This makes a decentralized learning-based framework appealing for sharing and aggregating learned knowledge in-between the local models that map the obtained metrics to the user QoE, such as Mean Opinion Scores (MOS). In this paper, we present a transfer learning-based ML model training approach, which allows decentralized local models to share generic indicators on MOS to learn a generic base model, and then customize the generic base model further using additional features that are unique to those specific localized (and potentially sensitive) QoE nodes. We show that the proposed approach is agnostic to specific ML algorithms, stacked upon each other, as it does not necessitate the collaborating localized nodes to run the same ML algorithm. Our reproducible results reveal the advantages of stacking various generic and specific models with corresponding weight factors. Moreover, we identify the optimal combination of algorithms and weight factors for the corresponding localized QoE nodes.

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