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, Qifan Gao, Linyan Jiang, Qing Zheng Luo, Jack Song, Yaqing Li, Summer Luo, Mae Chen, Stefan Liu, Danie Song, Huimin Zeng, Qi Chen, Ajeet Verma, Shweta Tripathi · 2025
This paper presents a comprehensive review of the 1stChallenge 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. Additional materials can be found on the project website11https://www.microsoft.com/en-us/research/academic-program/ntire-2025-vqe/,22https://github.com/varunj/cvpr-vqe/.