All-in-One: Joint Multimodal Image Compression via Shared 2D Gaussian Splatting

Aligned visible and thermal infrared images observe related boundaries but differ in texture and radiometric response. Independent compression repeats spatial support, whereas rigid sharing can suppress modality-specific structure. We study this trade-off with \method, an explicit RGB–IR codec that stores shared 2D Gaussian anchors, a jointly coded appearance dictionary, and sparse infrared geometry corrections. Sparse appearance routes are optimized in their deployed topology, and the correction support is fixed before its values are learned. Reconstruction uses normalized 2D splatting without a neural synthesis network. Complete-stream accounting separates representational savings from payload estimates and evaluates RGB and IR quality independently. In a ten-pair LLVIP development study, sparse corrections improve recorded RGB/IR PSNR by 0.129/0.299 dB over hard sharing at an additional 0.0189 pair-bpp; both SSIM values also improve. These component results motivate a controlled study of support reuse, with dataset-level matched-rate and complete-encoding-cost comparisons specified separately from the development evidence.