Text Prompted Image Coding for Machine

HUANG Zhimeng; GAO Feng; YANG Fan; MA Siwei

doi:10.12263/DZXB.20250778

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Text Prompted Image Coding for Machine

PAPERS | 更新时间：2026-06-04

- Text Prompted Image Coding for Machine
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 19-31(2026)
- 作者机构：
  
  1.北京大学计算机学院，北京 100871
  2.北京大学艺术学院，北京 100871
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62025101;62176006);China Postdoctoral Science Foundation(2025M771511)
- DOI：10.12263/DZXB.20250778
  CLC： TP37;TP39
- Received：07 September 2025，
  
  Accepted：19 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
黄志勐, 高峰, 杨帆, 等. 面向机器视觉的文本提示引导的图像编码[J]. 电子学报, 2026, 54(01): 19-31.

HUANG Zhimeng, GAO Feng, YANG Fan, et al. Text Prompted Image Coding for Machine[J]. Acta Electronica Sinica, 2026, 54(01): 19-31.
黄志勐, 高峰, 杨帆, 等. 面向机器视觉的文本提示引导的图像编码[J]. 电子学报, 2026, 54(01): 19-31. DOI：10.12263/DZXB.20250778

HUANG Zhimeng, GAO Feng, YANG Fan, et al. Text Prompted Image Coding for Machine[J]. Acta Electronica Sinica, 2026, 54(01): 19-31. DOI：10.12263/DZXB.20250778

摘要

近年来，随着物联网（Internet of Things，IoT）、语义通信以及智慧城市等经典机器间通信（Machine to Machine，M2M）场景的快速发展，海量视觉数据在设备间的实时传输与高效处理成为了一项关键挑战。在此背景下，传统以人眼感知质量为核心的图像编码方法，因其优化目标与机器视觉任务需求存在本质差异，往往在面向机器视觉分析时出现分析精度不足的问题。为此，面向机器视觉的图像编码（Image Coding for Machine，ICM）应运而生，其核心目标是在保证下游机器视觉任务（如分类、检测、分割等）分析精度的同时，实现尽可能低的编码码率，从而更好地适配M2M场景中的带宽与存储约束。然而，现有ICM方法仍面临两大瓶颈：其一，在极低码率条件下性能急剧下降。这是由于现有方法多依赖于端到端的非线性变换提取视觉特征，未能充分挖掘和利用图像中高层语义信息的紧凑表示，导致特征编码效率不足；其二，在开放场景下的泛化能力弱。多数方法针对单一任务、单一数据集进行优化，缺乏对未知类别、跨域数据的适应能力，难以在实际动态环境中保持稳定的分析性能。为突破上述限制，本文提出一种文本提示引导的面向机器视觉图像编码框架（Text-prompted Image Coding for Machine，T-ICM）。该框架的核心思想是将图像信息解耦为语义信息与纹理信息两个互补的组成部分，其中，语义信息以结构化文本提示（如对象类别、位置描述）的形式进行表示与编码，纹理信息则通过一种任务无关的通用视觉特征进行提取与压缩。在编码端，文本提示因其高度抽象和语义紧凑的特性，可以显著降低整体码率；通用特征则通过我们提出的分组特征编码模块进行高效压缩。在解码端，文本提示不仅用于直接解析完成分类、检测等任务，更重要的是作为引导信号，通过提示编码器与掩膜解码器，动态调整重建通用特征的语义感知区域，实现特征层面的域自适应与任务适配，从而显著提升模型在开放场景下的鲁棒性。本文在多个标准数据集与任务上对T-ICM进行了全面评估。实验表明，在语义分割和实例分割等密集预测任务上，T-ICM在极低码率下仍能保持接近原始图像输入的分析精度，其性能显著优于H.266/VVC、基于深度学习的图像编码器以及现有的其他ICM方法。本研究通过将语义信息迁移至高度压缩的文本模态进行传输，并利用其引导特征重建，T-ICM在编码效率与任务性能之间实现了更优的权衡，为未来语义通信、边缘智能协同，以及自适应机器视觉系统的发展提供了新的思路与技术支撑。

Abstract

In recent years

with the rapid development of classic machine-to-machine (M2M) communication scenarios such as the internet of things (IoT)

semantic communication

and smart cities

the real-time transmission and efficient processing of massive visual data between devices have become a critical challenge. In this context

traditional image coding methods

which are primarily optimized for human perceptual quality

often suffer from insufficient analysis accuracy when applied to machine vision tasks due to a fundamental mismatch between their optimization objectives and the requirements of machine analysis. Consequently

image coding for machine (ICM) has emerged

aiming to maintain high analysis accuracy for downstream machine vision tasks (e.g.

classification

detection

segmentation) while achieving the lowest possible bitrate

thereby better adapting to the bandwidth and storage constraints in M2M scenarios. However

existing ICM methods still face two major bottlenecks. First

their performance degrades sharply under extremely low bitrates. This is because most current approaches rely on end-to-end nonlinear transformations to extract visual features

failing to fully exploit the compact representation of high-level semantic information within images

which leads to inefficient feature coding. Second

they exhibit weak generalization in open-set scenarios. Most methods are optimized for single tasks or single datasets

lacking the adaptability to unseen categories or cross-domain data

and thus struggle to maintain stable analytical performance in practical

dynamic environments. To overcome these limitations

this paper proposes a novel text-prompted image coding for machine (T-ICM) framework. The core idea is to decouple image information into two complementary components: semantic information and texture information. The semantic information is represented and encoded in the form of structured text prompts (e.g.

object categories

location descriptions)

while the texture information is extracted and compressed as task-agnostic general visual features. At the encoder side

the text prompts

owing to their highly abstract and semantically compact nature

can significantly reduce the overall bitrate. The general features are efficiently compressed via our proposed grouped feature coding module. At the decoder side

the text prompts serve not only for direct parsing to accomplish tasks like classification and detection but

more importantly

act as guidance signals. Through a prompt encoder and a mask decoder

they dynamically adjust the semantically relevant regions of the reconstructed general features

enabling feature-level domain adaptation and task-specific adaptation

thereby significantly enhancing the model’s robustness in open-set scenarios. The proposed T-ICM is comprehensively evaluated on multiple standard datasets and tasks. Experiments demonstrate that on dense prediction tasks such as semantic segmentation and instance segmentation

T-ICM can maintain analysis accuracy close to that of using the original uncompressed images even at very low bitrates

significantly outperforming H.266/VVC

learned image codecs

and other existing ICM methods. By migrating semantic information to the highly compressed text modality for transmission and utilizing it to guide feature reconstruction

T-ICM achieves a superior trade-off between coding efficiency and task performance. This work provides a novel perspective and technical foundation for the future development of semantic communication

collaborative edge intelligence

and adaptive machine vision systems.

关键词

Keywords

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