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

2025-05-25

AI Summary

Real-world video conferencing suffers from low illumination, color distortion, prominent noise, and blurred details. Method: This paper proposes an end-to-end spatiotemporal lightweight Video Quality Enhancement (VQE) model that jointly optimizes illumination equalization, color fidelity, noise suppression, and sharpness enhancement. It introduces the first multi-objective VQE benchmark tailored to authentic meeting scenarios and incorporates a differentiable Video Quality Assessment (VQA) model to guide end-to-end training. A dual-track evaluation framework—combining objective metrics with crowdsourced subjective assessment—is also designed. Contribution/Results: In a public competition with 91 participating teams and 10 valid submissions, the top-performing method achieves significant improvements in key perceptual metrics—including LPIPS, NIQE, and MOS—demonstrating the feasibility and practicality of achieving studio-grade visual quality under low-bandwidth constraints.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Enhancing video quality in video conferencing scenarios
• Improving lighting, colors, noise reduction, and sharpness
• Developing a Video Quality Enhancement (VQE) model

Innovation

• Enhance video quality via lighting improvement
• Improve colors and reduce noise effectively
• Use VQA model for quality assessment