As video transmission increasingly serves machine vision systems (MVS) instead of human vision systems (HVS), video coding for machines (VCM) has become a critical research topic. Existing VCM methods often bind codecs to specific downstream models, requiring retraining or supervised data and thus limiting generalization in multi-task scenarios. Recently, unified VCM frameworks have employed visual backbones (VB) and visual foundation models (VFM) to support multiple video understanding tasks with a single codec. They mainly utilize VB/VFM to maintain semantic consistency or suppress non-semantic information, but seldom explore how to directly link video coding with understanding under VB/VFM guidance. Hence, we propose a Symmetric Entropy-Constrained Video Coding framework for Machines (SEC-VCM). It establishes a symmetric alignment between the video codec and VB, allowing the codec to leverage VB's representation capabilities to preserve semantics and discard MVS-irrelevant information. Specifically, a bi-directional entropy-constraint (BiEC) mechanism ensures symmetry between the process of video decoding and VB encoding by suppressing conditional entropy. This helps the codec to explicitly handle semantic information beneficial for MVS while squeezing useless information. Furthermore, a semantic-pixel dual-path fusion (SPDF) module injects pixel-level priors into the final reconstruction. Through semantic-pixel fusion, it suppresses artifacts harmful to MVS and improves machine-oriented reconstruction quality. Experimental results show our framework achieves state-of-the-art (SOTA) in rate-task performance, with significant bitrate savings over VTM on video instance segmentation (37.41%), video object segmentation (29.83%), object detection (46.22%), and multiple object tracking (44.94%). We will release our code.

翻译：随着视频传输日益服务于机器视觉系统而非人类视觉系统，面向机器的视频编码已成为关键研究课题。现有方法通常将编解码器与特定下游模型绑定，需要重新训练或监督数据，从而限制了其在多任务场景中的泛化能力。近期，统一框架采用视觉主干网络和视觉基础模型，以单一编解码器支持多种视频理解任务。这些方法主要利用视觉主干/基础模型来保持语义一致性或抑制非语义信息，但很少探索如何在视觉主干/基础模型指导下直接将视频编码与理解过程关联。为此，我们提出一种对称熵约束机器视频编码框架。该框架在视频编解码器与视觉主干网络之间建立对称对齐，使编解码器能够利用视觉主干的表征能力来保留语义并丢弃机器视觉无关信息。具体而言，双向熵约束机制通过抑制条件熵，确保视频解码过程与视觉主干编码过程的对称性。这有助于编解码器显式处理对机器视觉有益的语义信息，同时压缩无用信息。此外，语义-像素双路径融合模块将像素级先验注入最终重建过程，通过语义-像素融合抑制对机器视觉有害的伪影，提升面向机器的重建质量。实验结果表明，本框架在码率-任务性能上达到最优水平，在视频实例分割、视频目标分割、目标检测和多目标跟踪任务上，相比VTM分别节省37.41%、29.83%、46.22%和44.94%的码率。我们将公开代码。