用强化学习分配比特率，实现高分辨率遥感图像高效压缩
A PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression
- 基于PPO算法动态分配每块图像的比特率，优化压缩效率
- 在DIV2K和无人机数据集上分别达到19.3倍和21.2倍压缩比
- 保留关键任务信息，适用于城市监测等下游视觉任务
现有遥感图像压缩方法仍难以在高压缩率与细节保留之间取得平衡。高分辨率无人机影像虽能提供城市监测与灾情评估所需结构信息，但大范围数据集可达数百吉字节，给存储与长期管理带来挑战。本文提出一种基于PPO的比特率分配条件扩散压缩框架（PCDC），将条件扩散解码器与基于PPO的块级比特率分配策略结合，实现高压缩比的同时保持良好感知质量。我们还发布了包含沿海居民区低空拍摄的高分辨率无人机图像数据集。实验表明，该方法在DIV2K上实现19.3倍压缩，在无人机数据集上达21.2倍；下游目标检测任务显示重构图像保留了任务相关信息，性能损失可忽略。
原文摘要 · Abstract (English)
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.
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