| --- |
| license: mit |
| task_categories: |
| - image-to-image |
| language: |
| - en |
| tags: |
| - image-compression |
| - diffusion |
| - gaussian-blur |
| - zero-cost-encoder |
| --- |
| |
| # Diffusion-Based Image Compression with Zero-Cost Encoders |
|
|
| This repository contains model checkpoints for the paper: |
| **"Classical Transformations as Zero-Cost Encoders for Diffusion-Based Image Compression: A Study with Gaussian Blur"** |
| (Submitted to BMVC 2026) |
|
|
| ## Checkpoints |
|
|
| We release the σ=1 checkpoint for each domain — the practical operating point achieving 20-28% compression with strong perceptual quality across all domains. |
|
|
| | Domain | File | PSNR | LPIPS | Compression | |
| |--------|------|------|-------|-------------| |
| | MURA X-ray | `xray_sigma1/cond_step_600000.pt` | 48.81 dB | 0.006 | 26.5% | |
| | BraTS MRI | `brats_sigma1/cond_step_600000.pt` | 49.20 dB | 0.002 | 28.3% | |
| | CelebA | `celeba_sigma1/cond_step_582000.pt` | 42.01 dB | 0.004 | 20.3% | |
| | Buildings | `buildings_sigma1/cond_step_600000.pt` | 38.50 dB | 0.007 | 23.7% | |
|
|
| ## Usage |
|
|
| Evaluation scripts are provided in the supplementary material of the paper. To run inference on a single image: |
|
|
| ```bash |
| KMP_DUPLICATE_LIB_OK=TRUE python eval_single.py /path/to/image.png |
| ``` |
|
|
| The script auto-detects the dataset from the path and uses the appropriate checkpoint. |
|
|
| ## Datasets |
|
|
| - **MURA X-ray**: https://stanfordmlgroup.github.io/competitions/mura/ |
| - **BraTS**: https://www.synapse.org/Synapse:syn51156910 |
| - **CelebA**: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html |
| - **LSUN Buildings**: https://www.yf.io/p/lsun |
|
|
| ## Requirements |
|
|
| ``` |
| torch |
| torchvision |
| Pillow |
| numpy |
| scikit-image |
| lpips |
| ``` |
