🤖 AI Summary
Satellite imagery generates hundreds of terabytes of data daily, yet conventional compression methods apply uniform processing across entire images, struggling to balance downstream task requirements with compression efficiency. This work proposes a saliency-driven, region-adaptive compression approach that leverages saliency maps to guide spatially varying smoothing kernels during preprocessing, followed by standard lossy compression codecs such as JPEG. By integrating saliency-aware preprocessing with widely adopted compression standards, the method enables task-oriented variable bitrate allocation across image regions. It is the first to combine perceptual saliency with generic compression pipelines, significantly reducing storage and transmission overhead while preserving performance on downstream tasks—thereby overcoming the limitations inherent in globally uniform compression strategies.
📝 Abstract
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.