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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.01190 |
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| _version_ | 1866915223000055808 |
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| author | Zhu, Jingwen Chen, Yixu Wei, Hai Sethuraman, Sriram Wu, Yongjun |
| author_facet | Zhu, Jingwen Chen, Yixu Wei, Hai Sethuraman, Sriram Wu, Yongjun |
| contents | In adaptive bitrate streaming, resolution cross-over refers to the point on the convex hull where the encoding resolution should switch to achieve better quality. Accurate cross-over prediction is crucial for streaming providers to optimize resolution at given bandwidths. Most existing works rely on objective Video Quality Metrics (VQM), particularly VMAF, to determine the resolution cross-over. However, these metrics have limitations in accurately predicting resolution cross-overs. Furthermore, widely used VQMs are often trained on subjective datasets collected using the Absolute Category Rating (ACR) methodologies, which we demonstrate introduces significant uncertainty and errors in resolution cross-over predictions. To address these problems, we first investigate different subjective methodologies and demonstrate that Pairwise Comparison (PC) achieves better cross-over accuracy than ACR. We then propose a novel metric, Resolution Cross-over Quality Loss (RCQL), to measure the quality loss caused by resolution cross-over errors. Furthermore, we collected a new subjective dataset (LSCO) focusing on live streaming scenarios and evaluated widely used VQMs, by benchmarking their resolution cross-over accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Video Quality Assessment for Resolution Cross-Over in Live Sports Zhu, Jingwen Chen, Yixu Wei, Hai Sethuraman, Sriram Wu, Yongjun Multimedia In adaptive bitrate streaming, resolution cross-over refers to the point on the convex hull where the encoding resolution should switch to achieve better quality. Accurate cross-over prediction is crucial for streaming providers to optimize resolution at given bandwidths. Most existing works rely on objective Video Quality Metrics (VQM), particularly VMAF, to determine the resolution cross-over. However, these metrics have limitations in accurately predicting resolution cross-overs. Furthermore, widely used VQMs are often trained on subjective datasets collected using the Absolute Category Rating (ACR) methodologies, which we demonstrate introduces significant uncertainty and errors in resolution cross-over predictions. To address these problems, we first investigate different subjective methodologies and demonstrate that Pairwise Comparison (PC) achieves better cross-over accuracy than ACR. We then propose a novel metric, Resolution Cross-over Quality Loss (RCQL), to measure the quality loss caused by resolution cross-over errors. Furthermore, we collected a new subjective dataset (LSCO) focusing on live streaming scenarios and evaluated widely used VQMs, by benchmarking their resolution cross-over accuracy. |
| title | Video Quality Assessment for Resolution Cross-Over in Live Sports |
| topic | Multimedia |
| url | https://arxiv.org/abs/2504.01190 |