360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866912483793436672 |
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| author | Telili, Ahmed Hamidouche, Wassim Farhat, Ibrahim Amirpour, Hadi Timmerer, Christian Khadraoui, Ibrahim Lu, Jiajie Van Le, The Baek, Jeonneung Lee, Jin Young Wei, Yiying Sun, Xiaopeng Gao, Yu Huangl, JianCheng Zhong, Yujie |
| author_facet | Telili, Ahmed Hamidouche, Wassim Farhat, Ibrahim Amirpour, Hadi Timmerer, Christian Khadraoui, Ibrahim Lu, Jiajie Van Le, The Baek, Jeonneung Lee, Jin Young Wei, Yiying Sun, Xiaopeng Gao, Yu Huangl, JianCheng Zhong, Yujie |
| contents | Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditional methods, such as compression and adaptive resolution, help but often compromise video quality and introduce artifacts that degrade the viewer experience. Additionally, the unique spherical geometry of 360-degree video presents challenges not encountered in traditional 2D video. To address these issues, we initiated the 360-degree Video Super Resolution and Quality Enhancement Challenge. This competition encourages participants to develop efficient machine learning solutions to enhance the quality of low-bitrate compressed 360-degree videos, with two tracks focusing on 2x and 4x super-resolution (SR). In this paper, we outline the challenge framework, detailing the two competition tracks and highlighting the SR solutions proposed by the top-performing models. We assess these models within a unified framework, considering quality enhancement, bitrate gain, and computational efficiency. This challenge aims to drive innovation in real-time 360-degree video streaming, improving the quality and accessibility of immersive visual experiences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results Telili, Ahmed Hamidouche, Wassim Farhat, Ibrahim Amirpour, Hadi Timmerer, Christian Khadraoui, Ibrahim Lu, Jiajie Van Le, The Baek, Jeonneung Lee, Jin Young Wei, Yiying Sun, Xiaopeng Gao, Yu Huangl, JianCheng Zhong, Yujie Image and Video Processing Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditional methods, such as compression and adaptive resolution, help but often compromise video quality and introduce artifacts that degrade the viewer experience. Additionally, the unique spherical geometry of 360-degree video presents challenges not encountered in traditional 2D video. To address these issues, we initiated the 360-degree Video Super Resolution and Quality Enhancement Challenge. This competition encourages participants to develop efficient machine learning solutions to enhance the quality of low-bitrate compressed 360-degree videos, with two tracks focusing on 2x and 4x super-resolution (SR). In this paper, we outline the challenge framework, detailing the two competition tracks and highlighting the SR solutions proposed by the top-performing models. We assess these models within a unified framework, considering quality enhancement, bitrate gain, and computational efficiency. This challenge aims to drive innovation in real-time 360-degree video streaming, improving the quality and accessibility of immersive visual experiences. |
| title | 360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2411.06738 |