Image Compression and Decompression Framework Based on Latent Diffusion
  Model for Breast Mammography
InChan Hwang MinJae Woo
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.