mlsanityData to 14 Oct 2026

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

arXiv 2505.18988 ↗First posted 25 May 2025cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 29 of its 53 references that are in the map. First posted on arXiv 25 May 2025.

Builds on

29 of 53 references.

References · 29

  1. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 991
  2. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
  3. 2016Optical Flow Estimation using a Spatial Pyramid Networkcited by 424
  4. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  5. 2022Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectivescited by 320
  6. 2020Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-timecited by 163
  7. 2017DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networkscited by 156
  8. 2025HVI: A New Color Space for Low-light Image Enhancementcited by 63
  9. 2024The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Reportcited by 57
  10. 2020Video Super Resolution Based on Deep Learning: A Comprehensive Surveycited by 54
  11. 2024NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Resultscited by 40
  12. 2017Aesthetic-Driven Image Enhancement by Adversarial Learningcited by 38
17 more
  1. 2025The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Reportcited by 26
  2. 2025NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Resultscited by 25
  3. 2024Look Ma, no markers: holistic performance capture without the hasslecited by 23
  4. 2025The Tenth NTIRE 2025 Image Denoising Challenge Reportcited by 21
  5. 2022NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Resultscited by 20
  6. 2022AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Resultscited by 20
  7. 2025NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Resultscited by 16
  8. 2025NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Resultscited by 14
  9. 2025NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Resultscited by 14
  10. 2024Taming Lookup Tables for Efficient Image Retouchingcited by 12
  11. 2025NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Resultscited by 12
  12. 2025NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Resultscited by 12
  13. 2022Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restorationcited by 11
  14. 2025NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Studycited by 11
  15. 2022A crowdsourcing approach to video quality assessmentcited by 8
  16. 2025NTIRE 2025 challenge on Text to Image Generation Model Quality Assessmentcited by 8
  17. 2023Region-Aware Portrait Retouching with Sparse Interactive Guidancecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.