# Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography

arXiv: https://arxiv.org/abs/2310.05299 · HTML: https://mlsanity.com/paper/2310.05299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Identifying Women with Mammographically-Occult Breast Cancer Leveraging GAN-Simulated Mammograms](https://mlsanity.com/paper/2109.12113/) (2021)
- [Compression of phase-only holograms with JPEG standard and deep learning](https://mlsanity.com/paper/1806.03811/) (2018)
- [Temporal and volumetric denoising via quantile sparse image prior](https://mlsanity.com/paper/1802.03943/) (2018)

## Abstract (arXiv)

This research presents a novel framework for the compression and decompression of medical images utilizing the Latent Diffusion Model (LDM). The LDM represents advancement over the denoising diffusion probabilistic model (DDPM) with a potential to yield superior image quality while requiring fewer computational resources in the image decompression process. A possible application of LDM and Torchvision for image upscaling has been explored using medical image data, serving as an alternative to traditional image compression and decompression algorithms. The experimental outcomes demonstrate that this approach surpasses a conventional file compression algorithm, and convolutional neural network (CNN) models trained with decompressed files perform comparably to those trained with original image files. This approach also significantly reduces dataset size so that it can be distributed with a smaller size, and medical images take up much less space in medical devices. The research implications extend to noise reduction in lossy compression algorithms and substitute for complex wavelet-based lossless algorithms.