NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results

Varun Jain; Zongwei Wu; Quan Zou; Louis Florentin; Henrik Turbell; Sandeep Siddhartha; Radu Timofte; others

NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results

Computer Vision and Pattern Recognition 2025-05-27 v1

Authors: Varun Jain , Zongwei Wu , Quan Zou , Louis Florentin , Henrik Turbell , Sandeep Siddhartha , Radu Timofte , others

View on arXiv ↗ PDF ↗

Abstract

This paper presents a comprehensive review of the 1st Challenge on Video Quality Enhancement for Video Conferencing held at the NTIRE workshop at CVPR 2025, and highlights the problem statement, datasets, proposed solutions, and results. The aim of this challenge was to design a Video Quality Enhancement (VQE) model to enhance video quality in video conferencing scenarios by (a) improving lighting, (b) enhancing colors, (c) reducing noise, and (d) enhancing sharpness - giving a professional studio-like effect. Participants were given a differentiable Video Quality Assessment (VQA) model, training, and test videos. A total of 91 participants registered for the challenge. We received 10 valid submissions that were evaluated in a crowdsourced framework.

Keywords

video quality enhancement video conferencing video quality assessment NTIRE 2025 image restoration

Cite

@article{arxiv.2505.18988,
  title  = {NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results},
  author = {Varun Jain and Zongwei Wu and Quan Zou and Louis Florentin and Henrik Turbell and Sandeep Siddhartha and Radu Timofte and others},
  journal= {arXiv preprint arXiv:2505.18988},
  year   = {2025}
}

NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results

Abstract

Keywords

Cite

Related papers