Task-Aware Encoder Control for Deep Video Compression
Xingtong Ge Jixiang Luo Xinjie Zhang Tongda Xu Guo Lu Dailan He Jing Geng Yan Wang Jun Zhang Hongwei Qin
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.