A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction

FANG Xin; WANG Wei; HE Lijun; LI Fan

doi:10.7652/xjtuxb202607017

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A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction

更新时间：2026-07-09

- A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction
- Journal of Xi'an Jiaotong University Vol. 60, Issue 7, Pages: 185-195(2026)
- 作者机构：
  
  西安交通大学电子与信息学部，710049，西安
  中国移动通信集团陕西有限公司，710077，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202607017
  CLC： TP391
- Received：27 July 2025，
  
  Published：10 July 2026
- 稿件说明：
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FANG Xin, WANG Wei, HE Lijun, et al. A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 185-195.
DOI：

FANG Xin, WANG Wei, HE Lijun, et al. A Structure-Semantics-Residual Collaborative Image Compression Framework：Towards Privacy Protection and High-Fidelity Reconstruction[J]. Journal of Xi'an Jiaotong University, 2026, 60(7): 185-195. DOI： 10.7652/xjtuxb202607017.

摘要

为在极低码率条件下同时实现图像隐私保护与高保真重建，提出了一种结构-语义-残差协同的图像压缩框架。该方法从图像结构、语义与残差3个层面进行表征提取：利用边缘图作为结构信息进行主干压缩；提取去互信息处理的图像文本描述作为语义先验进行指导；将包含敏感区域信息的残差信号通过可信通道传输。在解码端，一方面结合边缘图与语义信息生成去隐私化图像，另一方面利用边缘图与残差信息恢复高保真原始图像，从而实现隐私安全与视觉保真的协同优化。结果表明：在CLIC数据集上的对比实验中，所提方法在实现高保真图像重建的同时，可节省13%～32%的码率，并能够分路生成去隐私化图像；消融实验验证了各个分支结构及约束机制的有效性，表明该框架能够在隐私保护与图像重建质量之间实现合理权衡。

Abstract

To achieve both image privacy protection and high-fidelity reconstruction at ultra-low bitrates，a structure-semantics-residual collaborative image compression framework is proposed. In this framework，representations are extracted from three distinct levels（i.e.，image structure，semantics and residual）：structural information is captured via edge maps for backbone compression；semantic priors are derived from image captions with mutual information removed；and residual signals containing sensitive region information are transmitted via a trusted channel.At the decoder，de-identified images are generated by integrating edge maps with semantic information，while original high-fidelity images are restored using edge maps and residual signals，thereby achieving synergistic optimization of privacy protection and visual fidelity.Results demonstrate that through the comparative experiments on the CLIC（Challenge on Learned Image Compression）dataset，the proposed method reduces bitrate by 13%—32% while maintaining high-quality reconstruction. Additionally，branch-wise generation of de-identified images is supported.Furthermore，the effectiveness of the individual branches and constraint mechanisms is verified through ablation experiments，indicating that a reasonable trade-off between privacy protection and image reconstruction quality is realized by the proposed framework.

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