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

2025/05/25 by Varun Jain, Zongwei Wu, Jain, Varun +12 · 8 citations
Computer Science · Social Sciences · #Advanced Computing and Algorithms #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Video Quality Assessment #Quality (philosophy) #Subjective video quality #Teleconference #Video processing #Video quality #Video recording #Videoconferencing

paper · pdf · doi:10.48550/arxiv.2505.18988

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

• NTIRE 2025 Image Shadow Removal Challenge Report
• NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
• NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces
• NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
• NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
• RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
• NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
• NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
• NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
• NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
• NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study
• NTIRE 2025 Challenge on Image Super-Resolution (×4): Methods and Results
• NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
• The Tenth NTIRE 2025 Image Denoising Challenge Report
• NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
• The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
• NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
• HVI: A New Color Space for Low-light Image Enhancement
• The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
• NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results
• Taming Lookup Tables for Efficient Image Retouching
• Region-Aware Portrait Retouching with Sparse Interactive Guidance
• Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
• AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
• Exploring CLIP for Assessing the Look and Feel of Images
• Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
• NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
• A crowdsourcing approach to video quality assessment
• Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
• BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
• Video Super Resolution Based on Deep Learning: A Comprehensive Survey
• Aesthetic-Driven Image Enhancement by Adversarial Learning
• DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
• Optical Flow Estimation using a Spatial Pyramid Network
• Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons

Cited by

• DeDelayed: Deleting Remote Inference Delay via On-Device Correction
• NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
• NTIRE 2025 Image Shadow Removal Challenge Report
• NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces
• NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
• NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
• NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
• RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report
• NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
• NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
• NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results
• NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
• NTIRE 2025 Challenge on Image Super-Resolution (×4): Methods and Results
• NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
• NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
• The Tenth NTIRE 2025 Image Denoising Challenge Report
• NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results
• The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report

Related

• NTIRE 2023 Quality Assessment of Video Enhancement Challenge
• NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results
• NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
• NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
• NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results