A Tri-Dynamic Preprocessing Framework for UGC Video Compression
This addresses the problem of handling variable UGC videos for internet traffic optimization, but it appears incremental as it builds on existing preprocessing and encoding methods.
The paper tackled the challenge of optimizing video compression for user-generated content (UGC) by proposing a Tri-Dynamic Preprocessing framework, which achieved exceptional performance on large-scale test sets.
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.