Encoding Complexity Control for Live Video Applications: An Interpretable Machine Learning Approach

In this paper, we propose an interpretable machine learning-based complexity control method for efficiently im-plementing HEVC on live video applications with different computing capacities and limited powers. Specifically, a complexity allocation method is designed to reasonably assign the complexity resources. Then, a multi-accuracy Coding Unit (CU) decision model is obtained by interpret-ably adjusting the parameters to efficiently and flexibly achieve a tradeoff between encoding complexity and rate distortion performance. Finally, a coding tree unit-level complexity control method is proposed to select appropri-ate accuracy of the CU decision model for making the en-coding complexity approach the target. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of accuracy and encoding efficiency.