ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866911058603540480 |
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| author | Chen, Yixu Chen, Bowen Wei, Hai Bovik, Alan C. Li, Baojun Sun, Wei Cao, Linhan Fu, Kang Zhu, Dandan Jia, Jun Hu, Menghan Min, Xiongkuo Zhai, Guangtao Hammou, Dounia Yin, Fei Mantiuk, Rafal Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V |
| author_facet | Chen, Yixu Chen, Bowen Wei, Hai Bovik, Alan C. Li, Baojun Sun, Wei Cao, Linhan Fu, Kang Zhu, Dandan Jia, Jun Hu, Menghan Min, Xiongkuo Zhai, Guangtao Hammou, Dounia Yin, Fei Mantiuk, Rafal Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V |
| contents | This paper reports IEEE International Conference on Multimedia \& Expo (ICME) 2025 Grand Challenge on Generalizable HDR and SDR Video Quality Measurement. With the rapid development of video technology, especially High Dynamic Range (HDR) and Standard Dynamic Range (SDR) contents, the need for robust and generalizable Video Quality Assessment (VQA) methods has become increasingly demanded. Existing VQA models often struggle to deliver consistent performance across varying dynamic ranges, distortion types, and diverse content. This challenge was established to benchmark and promote VQA approaches capable of jointly handling HDR and SDR content. In the final evaluation phase, five teams submitted seven models along with technical reports to the Full Reference (FR) and No Reference (NR) tracks. Among them, four methods outperformed VMAF baseline, while the top-performing model achieved state-of-the-art performance, setting a new benchmark for generalizable video quality assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge Chen, Yixu Chen, Bowen Wei, Hai Bovik, Alan C. Li, Baojun Sun, Wei Cao, Linhan Fu, Kang Zhu, Dandan Jia, Jun Hu, Menghan Min, Xiongkuo Zhai, Guangtao Hammou, Dounia Yin, Fei Mantiuk, Rafal Premkumar, Amritha Rajendran, Prajit T Menon, Vignesh V Image and Video Processing Computer Vision and Pattern Recognition Multimedia This paper reports IEEE International Conference on Multimedia \& Expo (ICME) 2025 Grand Challenge on Generalizable HDR and SDR Video Quality Measurement. With the rapid development of video technology, especially High Dynamic Range (HDR) and Standard Dynamic Range (SDR) contents, the need for robust and generalizable Video Quality Assessment (VQA) methods has become increasingly demanded. Existing VQA models often struggle to deliver consistent performance across varying dynamic ranges, distortion types, and diverse content. This challenge was established to benchmark and promote VQA approaches capable of jointly handling HDR and SDR content. In the final evaluation phase, five teams submitted seven models along with technical reports to the Full Reference (FR) and No Reference (NR) tracks. Among them, four methods outperformed VMAF baseline, while the top-performing model achieved state-of-the-art performance, setting a new benchmark for generalizable video quality assessment. |
| title | ICME 2025 Generalizable HDR and SDR Video Quality Measurement Grand Challenge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2506.22790 |