AI RESEARCH
Adaptive Learned Image Compression with Graph Neural Networks
arXiv CS.CV
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ArXi:2603.25316v1 Announce Type: new Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple non-redundant pixels simply due to their proximity in Euclidean space.