{"ID":2824127,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24463","arxiv_id":"2512.24463","title":"Spectral and Spatial Graph Learning for Multispectral Solar Image Compression","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 https://github.com/agyat4/sgraph .","short_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 I...","url_abs":"https://arxiv.org/abs/2512.24463","url_pdf":"https://arxiv.org/pdf/2512.24463v1","authors":"[\"Prasiddha Siwakoti\",\"Atefeh Khoshkhahtinat\",\"Piyush M. Mehta\",\"Barbara J. Thompson\",\"Michael S. F. Kirk\",\"Daniel da Silva\"]","published":"2025-12-30T20:54:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":605548,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2824127,"paper_url":"https://arxiv.org/abs/2512.24463","paper_title":"Spectral and Spatial Graph Learning for Multispectral Solar Image Compression","repo_url":"https://github.com/agyat4/sgraph","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}