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.
@article{arxiv.2404.04848,
title = {Task-Aware Encoder Control for Deep Video Compression},
author = {Xingtong Ge and Jixiang Luo and Xinjie Zhang and Tongda Xu and Guo Lu and Dailan He and Jing Geng and Yan Wang and Jun Zhang and Hongwei Qin},
journal= {arXiv preprint arXiv:2404.04848},
year = {2024}
}