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 https://github.com/agyat4/sgraph .

翻译：多光谱太阳图像的高保真压缩对空间任务而言仍具挑战性，需在有限带宽与保留精细光谱及空间细节之间取得平衡。本文提出一种专为太阳观测设计的深度学习图像压缩框架，该框架利用两个互补模块：(1) 光谱间窗口化图嵌入模块，通过将光谱通道表示为具有可学习边特征的图节点，显式建模波段间关系；(2) 窗口化空间图注意力与卷积块注意力模块，结合稀疏图注意力与卷积注意力机制，以降低空间冗余并增强精细结构。在SDOML数据集六个极紫外通道上的评估表明，相较于先进的深度学习基线方法，本方法在平均光谱信息散度上降低20.15%，峰值信噪比提升最高达1.09%，对数变换多尺度结构相似性增益达1.62%，在可比比特每像素率下实现了更清晰且光谱保真的重建。代码公开于 https://github.com/agyat4/sgraph。