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A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression

Yuming Han, Jooho Kim, Anish Shakya

Abstract

Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers valuable structural details for urban monitoring and disaster assessment, but large-area datasets can easily reach hundreds of gigabytes, creating significant challenges for storage and long-term management. In this paper, we propose a PPO-based bitrate allocation Conditional Diffusion Compression (PCDC) framework. PCDC integrates a conditional diffusion decoder with a PPO-based block-wise bitrate allocation strategy to achieve high compression ratios while maintaining strong perceptual performance. We also release a high-resolution drone image dataset with richer structural details at a consistent low altitude over residential neighborhoods in coastal urban areas. Experimental results show compression ratios of 19.3x on DIV2K and 21.2x on the drone image dataset. Moreover, downstream object detection experiments demonstrate that the reconstructed images preserve task-relevant information with negligible performance loss.

A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression

Abstract

Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers valuable structural details for urban monitoring and disaster assessment, but large-area datasets can easily reach hundreds of gigabytes, creating significant challenges for storage and long-term management. In this paper, we propose a PPO-based bitrate allocation Conditional Diffusion Compression (PCDC) framework. PCDC integrates a conditional diffusion decoder with a PPO-based block-wise bitrate allocation strategy to achieve high compression ratios while maintaining strong perceptual performance. We also release a high-resolution drone image dataset with richer structural details at a consistent low altitude over residential neighborhoods in coastal urban areas. Experimental results show compression ratios of 19.3x on DIV2K and 21.2x on the drone image dataset. Moreover, downstream object detection experiments demonstrate that the reconstructed images preserve task-relevant information with negligible performance loss.
Paper Structure (9 sections, 10 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 9 sections, 10 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Conditional Diffusion Based Compression Framework
  4. PPO-based Bitrate Allocation
  5. Numerical Results
  6. Experiments Setup
  7. Comparison Results
  8. Downstream Object Detection Performances
  9. Conclusion

Figures (5)

  • Figure 1: System model of the proposed diffusion-based image compression framework with PPO-based bitrate allocation.
  • Figure 2: Data collection points and sample images in Galveston, Texas.
  • Figure 3: Visual comparison of reconstruction images by different methods. The first two rows are from the proposed drone image dataset and the last row is from DIV2K. (a) BPG, (b) HiFiC, (c) CDC, and (d) the proposed PCDC. The proposed method reconstructs sharper edges and clearer structural details.
  • Figure 4: Rate--distortion comparison of BPG, HiFiC, CDC, and the proposed PCDCin terms of LPIPS, DISTS, and PSNR across different bitrates (bpp).
  • Figure 5: Visual comparisons with corresponding confidence scores for building (first row) and vehicle (second row) detections.