Dimensions overall score 8.0

• Existing state-of-the-art learned image compression methods based on CNNs or Transformers are inherently rigid due to fixed receptive fields and static connectivity patterns.limitation 70%
• Graph Neural Networks (GNNs) can be used to develop content-adaptive image compression frameworks.method 70%
• The proposed GLIC model constructs dual-scale graphs to enable flexible, data-driven receptive fields.method 70%
• GLIC introduces adaptive connectivity by dynamically adjusting the number of neighbors for each node based on local content complexity.method 70%
• GLIC achieves state-of-the-art performance in image compression.result 70%
• GLIC achieves BD-rate reductions of 19.29% relative to VTM-9.1 on the Kodak dataset.result 70%
• GLIC achieves BD-rate reductions of 21.69% relative to VTM-9.1 on the Tecnick dataset.result 70%
• GLIC achieves BD-rate reductions of 18.71% relative to VTM-9.1 on the CLIC dataset.result 70%

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