Abstract
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at this https URL .
Abstract (translated)
高保真多光谱太阳图像压缩对于太空任务来说仍然是一项挑战，因为在有限的带宽下必须权衡保留精细光谱和空间细节的需求。我们提出了一种针对太阳观测定制的学习型图像压缩框架，利用了两个互补模块：（1）跨频段窗口图嵌入 (iSWGE)，该模块通过将光谱通道表示为带有学习到的边特征的图节点来显式建模频带间的关联；（2）窗口空间图注意力与卷积块注意力 (WSGA-C) 模块，它结合了稀疏图注意力和卷积注意力以减少空间冗余并强调细小结构。在涵盖六个极紫外(EUV)通道的SDOML数据集上进行评估后显示，我们的方法实现了20.15% 的平均光谱信息散度 (MSID) 减少、高达1.09% 的峰值信噪比（PSNR）提升以及对强大学习基准相比提高1.62% 对数变换的多尺度结构相似性指标(MS-SSIM)，从而在相当的每像素比特率下提供了更清晰且光谱真实的重建图像。代码可在以下网址公开获取：[此处应填写实际链接，由于文本中未提供具体链接，请访问原文或相关发布页获取]。
URL
https://arxiv.org/abs/2512.24463
