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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 26036-26045

Abstract


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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Ge_2024_CVPR, author = {Ge, Xingtong and Luo, Jixiang and Zhang, Xinjie and Xu, Tongda and Lu, Guo and He, Dailan and Geng, Jing and Wang, Yan and Zhang, Jun and Qin, Hongwei}, title = {Task-Aware Encoder Control for Deep Video Compression}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {26036-26045} }