# LSTM-based Video Quality Prediction Accounting for Temporal Distortions in Videoconferencing Calls

arXiv: https://arxiv.org/abs/2303.12761 · HTML: https://mlsanity.com/paper/2303.12761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction](https://mlsanity.com/paper/2010.13715/) (2020)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network](https://mlsanity.com/paper/1910.13646/) (2019)
- [A crowdsourcing approach to video quality assessment](https://mlsanity.com/paper/2204.06784/) (2022)
- [Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory](https://mlsanity.com/paper/1703.00633/) (2017)

## Built on by (most cited first, 2 of 2)

- [Offline to Online Learning for Real-Time Bandwidth Estimation](https://mlsanity.com/paper/2309.13481/) (2023)
- [Human-in-the-Loop Bandwidth Estimation for Quality of Experience Optimization in Real-Time Video Communication](https://mlsanity.com/paper/2510.12265/) (2025)

## Abstract (arXiv)

Current state-of-the-art video quality models, such as VMAF, give excellent prediction results by comparing the degraded video with its reference video. However, they do not consider temporal distortions (e.g., frame freezes or skips) that occur during videoconferencing calls. In this paper, we present a data-driven approach for modeling such distortions automatically by training an LSTM with subjective quality ratings labeled via crowdsourcing. The videos were collected from live videoconferencing calls in 83 different network conditions. We applied QR codes as markers on the source videos to create aligned references and compute temporal features based on the alignment vectors. Using these features together with VMAF core features, our proposed model achieves a PCC of 0.99 on the validation set. Furthermore, our model outputs per-frame quality that gives detailed insight into the cause of video quality impairments. The VCM model and dataset are open-sourced at https://github.com/microsoft/Video_Call_MOS.