{"ID":2827356,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16101","arxiv_id":"2512.16101","title":"A Tri-Dynamic Preprocessing Framework for UGC Video Compression","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.16101","url_pdf":"https://arxiv.org/pdf/2512.16101v1","authors":"[\"Fei Zhao\",\"Mengxi Guo\",\"Shijie Zhao\",\"Junlin Li\",\"Li Zhang\",\"Xiaodong Xie\"]","published":"2025-12-18T02:38:52Z","proceeding":"cs.MM","tasks":"[\"cs.MM\",\"cs.CV\"]","methods":"[]","has_code":false}