Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task. In contrast, traditional video codecs employ a flexible encoder controller, enabling the adaptation of a single codec to different tasks through mechanisms like mode prediction. Drawing inspiration from this, we introduce an innovative encoder controller for deep video compression for machines. This controller features a mode prediction and a Group of Pictures (GoP) selection module. Our approach centralizes control at the encoding stage, allowing for adaptable encoder adjustments across different tasks, such as detection and tracking, while maintaining compatibility with a standard pre-trained DVC decoder. Empirical evidence demonstrates that our method is applicable across multiple tasks with various existing pre-trained DVCs. Moreover, extensive experiments demonstrate that our method outperforms previous DVC by about 25% bitrate for different tasks, with only one pre-trained decoder.

我们引入了一种创新的编码器控制器，它具有模式预测和图片组选择模块，通过在编码阶段集中控制，使得在不同任务中可以进行可调的编码器调整，同时保持与标准预训练深度视频压缩解码器的兼容性。实证数据表明，我们的方法适用于多个任务且适用于多个现有的预训练深度视频压缩解码器，而且广泛实验表明，我们的方法对于不同任务的比特率优于以前的深度视频压缩方法，仅需一个预训练解码器。

深度视频压缩的任务感知编码器控制