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  3. Symmetric Entropy-constrained Video Coding For Machines.
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  3. Symmetric Entropy-constrained Video Coding For Machines.

Related Experiment Videos

Symmetric Entropy-Constrained Video Coding for Machines.

Yuxiao Sun, Meiqin Liu, Chao Yao

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a novel Symmetric Entropy-Constrained Video Coding for Machines (SEC-VCM) framework. SEC-VCM enhances video coding for machine vision by aligning codecs with visual backbones, achieving significant bitrate savings and state-of-the-art performance.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Video transmission increasingly serves machine vision systems (MVS), necessitating specialized video coding for machines (VCM).
    • Existing VCM methods often lack generalization due to task-specific codec binding and reliance on retraining or supervised data.
    • Unified VCM frameworks using visual backbones (VB) and visual foundation models (VFM) show promise but seldom directly link coding with understanding.

    Purpose of the Study:

    • To propose a novel Symmetric Entropy-Constrained Video Coding framework for Machines (SEC-VCM).
    • To establish a symmetric alignment between video codecs and VBs for improved semantic preservation and irrelevant information discarding.
    • To enhance machine-oriented reconstruction quality by suppressing artifacts and injecting pixel-level priors.

    Main Methods:

    • Developed a Symmetric Entropy-Constrained Video Coding framework for Machines (SEC-VCM).
    • Implemented a bi-directional entropy-constraint (BiEC) mechanism for symmetric alignment between video decoding and VB encoding.
    • Introduced a semantic-pixel dual-path fusion (SPDF) module for integrating pixel-level priors and semantic information.

    Main Results:

    • Achieved state-of-the-art (SOTA) rate-task performance on classical video understanding and MLLM-based tasks.
    • Demonstrated significant bitrate savings compared to H.266/VVC reference software VTM across various tasks.
    • Reported average bitrate savings of 37.4% for video instance segmentation, 29.8% for video object segmentation, 46.2% for object detection, 44.9% for multiple object tracking, and 97.6% for MLLM-based video grounding.

    Conclusions:

    • The proposed SEC-VCM framework effectively leverages VB capabilities for semantic preservation and irrelevant information suppression in VCM.
    • The BiEC mechanism and SPDF module contribute to improved reconstruction quality and significant bitrate reduction for machine vision applications.
    • SEC-VCM offers a generalized and efficient solution for video coding tailored to machine vision systems.