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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16101 |
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| _version_ | 1866914206717050880 |
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| author | Zhao, Fei Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li Xie, Xiaodong |
| author_facet | Zhao, Fei Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li Xie, Xiaodong |
| contents | In recent years, user generated content (UGC) has become the dominant force in internet traffic. However, UGC videos exhibit a higher degree of variability and diverse characteristics compared to traditional encoding test videos. This variance challenges the effectiveness of data-driven machine learning algorithms for optimizing encoding in the broader context of UGC scenarios. To address this issue, we propose a Tri-Dynamic Preprocessing framework for UGC. Firstly, we employ an adaptive factor to regulate preprocessing intensity. Secondly, an adaptive quantization level is employed to fine-tune the codec simulator. Thirdly, we utilize an adaptive lambda tradeoff to adjust the rate-distortion loss function. Experimental results on large-scale test sets demonstrate that our method attains exceptional performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Tri-Dynamic Preprocessing Framework for UGC Video Compression Zhao, Fei Guo, Mengxi Zhao, Shijie Li, Junlin Zhang, Li Xie, Xiaodong Multimedia Computer Vision and Pattern Recognition In recent years, user generated content (UGC) has become the dominant force in internet traffic. However, UGC videos exhibit a higher degree of variability and diverse characteristics compared to traditional encoding test videos. This variance challenges the effectiveness of data-driven machine learning algorithms for optimizing encoding in the broader context of UGC scenarios. To address this issue, we propose a Tri-Dynamic Preprocessing framework for UGC. Firstly, we employ an adaptive factor to regulate preprocessing intensity. Secondly, an adaptive quantization level is employed to fine-tune the codec simulator. Thirdly, we utilize an adaptive lambda tradeoff to adjust the rate-distortion loss function. Experimental results on large-scale test sets demonstrate that our method attains exceptional performance. |
| title | A Tri-Dynamic Preprocessing Framework for UGC Video Compression |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16101 |