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.

翻译：近年来，用户生成内容已成为互联网流量的主导力量。然而，与传统编码测试视频相比，UGC视频表现出更高的变异性和多样化的特征。这种差异在更广泛的UGC场景下，对数据驱动的机器学习算法优化编码的有效性提出了挑战。为解决这一问题，我们提出了一种面向UGC的三重动态预处理框架。首先，我们采用自适应因子来调节预处理强度。其次，利用自适应量化级别对编解码器模拟器进行微调。第三，我们通过自适应lambda权衡来调整率失真损失函数。在大规模测试集上的实验结果表明，我们的方法取得了卓越的性能。