arXiv: cs.AI preprintsInternational6 November 2026
Neural Image Compression with Internal-External Prior Fusion and Alignment
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arXiv:2611.07644v1 Announce Type: cross Abstract: Existing context models for learned image com- pression rely primarily on internal priors from the current image, overlooking common patterns and typical textures shared across natural images. To address this limitation, we propose an Internal-External Prior Fusion-Alignment (IE-PFA) context model built upon quadtree spatial-channel joint modeling, where a learnable dictionary shared by the encoder and decoder serves as an external prior. Specifically, an RWKV-based Spatial- Channel Prior Fusion (RSCPF) module efficiently fuses inter- nal and e
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