# Formal Definition of QoE Metrics

arXiv: https://arxiv.org/abs/1607.00321 · HTML: https://mlsanity.com/paper/1607.00321/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2016.

## Built on by (most cited first, 8 of 8)

- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.03040/) (2022)
- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Deep learning in motion deblurring: current status, benchmarks and future prospects](https://mlsanity.com/paper/2401.05055/) (2024)
- [Maximum entropy and quantized metric models for absolute category ratings](https://mlsanity.com/paper/2410.00817/) (2024)
- [Zoom, Enhance! Measuring Surveillance GAN Up-sampling](https://mlsanity.com/paper/2108.09285/) (2021)
- [Full Body Video-Based Self-Avatars for Mixed Reality: from E2E System to User Study](https://mlsanity.com/paper/2208.12639/) (2022)

## Abstract (arXiv)

This technical report formally defines the QoE metrics which are introduced and discussed in the article "QoE Beyond the MOS: An In-Depth Look at QoE via Better Metrics and their Relation to MOS" by Tobias Hossfeld, Poul E. Heegaard, Martin Varela, Sebastian M"oller, accepted for publication in the Springer journal "Quality and User Experience". Matlab scripts for computing the QoE metrics for given data sets are available in GitHub.