用显著性图优化遥感图像压缩，重点区域保质，非重点区域降质。
Saliency Driven Imagery Preprocessing for Efficient Compression -- Industrial Paper
- 根据显著性图对图像分块处理，不同区域用不同平滑核
- 实现单张大图像内可变率压缩，压缩比提升20%以上
- 适合卫星图像处理、地理信息分析等只关注局部的场景
卫星图像每天产生数百太字节，压缩成本高昂。尽管图像分辨率不断提高，但下游任务通常只关注图像中少数区域。本文提出一种基于显著性图的预处理方法，将图像按重要性分块，使用不同大小的平滑核进行处理，以适配传统有损压缩标准。该方法在单幅大图像内实现可变率压缩，显著提升编码效率。实验表明，该策略可在保持关键区域质量的同时，有效降低非重点区域的数据量，提升整体压缩性能，适用于仅需局部信息的任务场景。
原文摘要 · Abstract (English)
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.
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