Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images

Enjia Gu, Yongshan Zhang, Xinxin Wang, Xinwei Jiang

Abstract: Lossless compression of remote sensing images is critically important for minimizing storage requirements while preserving the complete integrity of the data. The main challenge in lossless compression lies in striking a good balance between reasonable compression durations and high compression ratios. In this article, we introduce an innovative lossless compression framework that uniquely utilizes lossy compression data as prior knowledge to enhance the compression process. Our framework employs a checkerboard segmentation technique to divides the original remote sensing image into various subimages. The main diagonal subimages are compressed using a traditional lossy method to obtain prior knowledge for facilitating the compression of all subimages. These subimages are then subjected to lossless compression using our newly developed lossy prior probability prediction network (LP3Net) and arithmetic coding in a specific order. The proposed LP3Net is an advanced network architecture, consisting of an image preprocessing module, a channel enhancement module, and a pixel probability transformer module, to learn the discrete probability distribution of each pixel within every subimage, enhancing the accuracy and efficiency of the compression process. Experiments on high-resolution remote sensing image datasets demonstrate the effectiveness and efficiency of the proposed LP3Net and lossless compression framework, achieving a minimum of 4.57% improvement over traditional compression methods and 1.86% improvement over deep learning-based compression methods.