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

TL;DR

This paper reviews the NTIRE 2025 challenge on Video Quality Enhancement for conferencing, discussing datasets, methods, and results aimed at improving video quality for professional conferencing scenarios.

Contribution

It introduces a comprehensive challenge framework, datasets, and evaluation methods for advancing video quality enhancement techniques in conferencing.

Findings

10 valid submissions evaluated in a crowdsourced framework

Participants developed models improving lighting, colors, noise, and sharpness

The challenge fostered progress in real-world video enhancement methods

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.