The compression of satellite imagery remains an important research area as hundreds of terabytes of images are collected every day, which drives up storage and bandwidth costs. Although progress has been made in increasing the resolution of these satellite images, many downstream tasks are only interested in small regions of any given image. These areas of interest vary by task but, once known, can be used to optimize how information within the image is encoded. Whereas standard image encoding methods, even those optimized for remote sensing, work on the whole image equally, there are emerging methods that can be guided by saliency maps to focus on important areas. In this work we show how imagery preprocessing techniques driven by saliency maps can be used with traditional lossy compression coding standards to create variable rate image compression within a single large satellite image. Specifically, we use variable sized smoothing kernels that map to different quantized saliency levels to process imagery pixels in order to optimize downstream compression and encoding schemes.

翻译：卫星图像压缩仍然是一个重要的研究领域，因为每天收集的数百太字节图像推高了存储和带宽成本。尽管在提高这些卫星图像分辨率方面已取得进展，但许多下游任务仅对任意给定图像中的小区域感兴趣。这些关注区域因任务而异，但一旦确定，便可用来优化图像内信息的编码方式。标准的图像编码方法（即使是针对遥感优化的方法）均对整个图像进行同等处理，而新兴方法可通过显著性图引导来聚焦重要区域。本研究展示了如何将显著性图驱动的图像预处理技术与传统有损压缩编码标准结合，在单幅大型卫星图像中实现可变码率压缩。具体而言，我们采用与不同量化显著性级别映射的可变尺寸平滑核对图像像素进行处理，以优化下游压缩与编码方案。