Lossy compression of three-channel remote sensing images with controllable quality

In this paper, we consider a problem of lossy compression of three-channel or color images with application to remote sensing. The main task of such a compression is to provide a trade-off between compression ratio and quality of compressed data that should be appropriate for solving the basic tasks as classification of sensed terrains, object detection and so on. Then, alongside with a desire to increase compression ratio, one needs to control introduced distortions (image quality) to ensure that compressed data are appropriate for further use. We propose a way to lossy compression that is based on providing quality of compressed images not worse than desired according to quality metrics. The outcome of our approach is that classification accuracy either does not get worse than for uncompressed data (and sometimes even improves) or gets worse only slightly. Earlier, we have shown that it is possible to control quality for component-wise compression of multichannel images. Here, it is demonstrated that it is possible to control quality for 3D compression. Compared to component-wise compression, 3D approach leads to two important benefits: 1) compression ratio can be almost twice larger; 2) probability of correct classification can be slightly better. These benefits are confirmed for real-life three-component data acquired by Sentinel sensor using maximum likelihood-based classifier.