README.md

metadata

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:

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