Research map / Image generation / Image restoration
Neural Data-Dependent Transform for Learned Image Compression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add neural-syntax to the learned compression model (ablation) | Context-model learned image compression · Kodak | BD-rate: 9.17% in BD-rate | Helped |
| Enable continuous online mode decision by finetuning the encoder for each image (ablation) | Neural-syntax compression model · Kodak | BD-rate: 2.35% | Helped |
| Add neural-syntax-based post-processing (ablation) | Ours · Kodak | BD-rate vs BPG: -14.5% -> -20.1% | Helped |
| Add neural-syntax-based post-processing (ablation) | Ours · CLIC Professional Validation | BD-rate vs BPG: -25.3% -> -29.7% | Helped |
Closest papers by meaning
- How To Exploit the Transferability of Learned Image Compression to Conventional Codecs (CVPR 2021 · similarity 0.89)
- End-to-end Optimized Image Compression (ICLR 2017 · similarity 0.89)
- Towards Backward-Compatible Continual Learning of Image Compression (CVPR 2024 · similarity 0.88)
- GLIC: General Format Learned Image Compression (AAAI 2025 · similarity 0.88)
- Controllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression (AAAI 2025 · similarity 0.88)
- Robustly overfitting latents for flexible neural image compression (NeurIPS 2024 · similarity 0.88)
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