针对用户生成视频压缩难题，提出三动态预处理框架提升编码效果。
A Tri-Dynamic Preprocessing Framework for UGC Video Compression
- 根据内容特性自适应调节预处理强度。
- 在大规模测试集上显著优于传统方法。
- 适合需要高效压缩UGC视频的场景。
近年来，用户生成内容（UGC）已成为互联网流量的主要来源。然而，与传统编码测试视频相比，UGC视频具有更高的多样性和复杂特征，这种差异性挑战了数据驱动机器学习算法在更广泛UGC场景中优化编码的有效性。为此，我们提出一种针对UGC的三动态预处理框架。首先，采用自适应因子调节预处理强度；其次，使用自适应量化级别微调编解码器模拟器；第三，利用自适应lambda权衡调整率失真损失函数。在大规模测试集上的实验结果表明，该方法表现出卓越性能。
原文摘要 · Abstract (English)
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.
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