NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910967452925952 |
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| author | Jain, Varun Wu, Zongwei Zou, Quan Florentin, Louis Turbell, Henrik Siddhartha, Sandeep Timofte, Radu others |
| author_facet | Jain, Varun Wu, Zongwei Zou, Quan Florentin, Louis Turbell, Henrik Siddhartha, Sandeep Timofte, Radu others |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results Jain, Varun Wu, Zongwei Zou, Quan Florentin, Louis Turbell, Henrik Siddhartha, Sandeep Timofte, Radu others Computer Vision and Pattern Recognition 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. |
| title | NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18988 |