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Symmetric Entropy-Constrained Video Coding for Machines
Summary
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.
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.
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