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, 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 to 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 on classical video understanding tasks and MLLM-based tasks show state-of-the-art (SOTA) rate-task performance. It achieves significant bitrate savings over H.266/VVC reference software VTM on video instance segmentation (37.4%), video object segmentation (29.8%), object detection (46.2%), multiple object tracking (44.9%), and MLLM-based video grounding (97.6%). The code is at https://github.com/Ws-Syx/SEC-VCM.

Currently, video transmission serves not only the Human Visual System (HVS) for viewing but also machine perception for analysis. However, existing codecs are primarily optimized for pixel-domain and HVS-perception metrics rather than the needs of machine vision tasks. To address this issue, we propose a Compression Distortion Representation Embedding (CDRE) framework, which extracts machine-perception-related distortion representation and embeds it into downstream models, addressing the information lost during compression and improving task performance. Specifically, to better analyze the machine-perception-related distortion, we design a compression-sensitive extractor that identifies compression degradation in the feature domain. For efficient transmission, a lightweight distortion codec is introduced to compress the distortion information into a compact representation. Subsequently, the representation is progressively embedded into the downstream model, enabling it to be better informed about compression degradation and enhancing performance. Experiments across various codecs and downstream tasks demonstrate that our framework can effectively boost the rate-task performance of existing codecs with minimal overhead in terms of bitrate, execution time, and number of parameters. Our codes and supplementary materials are released in https://github.com/Ws-Syx/CDRE/.

Current research on grasping state analysis in soft manipulators is limited and lacks broad applicability. In this article, we introduce a novel method that leverages multimodal data from flexible sensors and Inertial Measurement Units (IMUs) to develop a comprehensive grasping state analysis system for multidegree-of-freedom (multi-DOF) pneumatic soft manipulators. A Deep Spiking High-Dimensional Fuzzy Network (DSHTFN) algorithm is specifically designed to analyze the “3S” grasping states of soft manipulators—shaking, stable, and slipping—with greater depth and precision. A novel membership function, the BernoulliArctangent (B-Atan) function, has been designed to accommodate the unique characteristics of spiking input signals and support backpropagation capabilities. Experimental results demonstrate that our proposed method achieves accuracies of 95.66% and 96.05% in opposing-finger and three-fingered soft manipulator operations, respectively. Through comparative analysis with other algorithms, the superior performance of the B-Atan membership function and the DSHTFN approach in analyzing the grasping states of soft manipulators has been validated.