You can compress images for specific AI tasks (like object detection) without retraining by steering a pretrained diffusion model's denoising process with task guidance signals—enabling one codec to serve multiple purposes.
This paper presents Flex-DDCM, a zero-shot image compression method that uses diffusion models to compress images while optimizing for specific downstream tasks. By guiding the diffusion denoising process with task-specific signals, it achieves flexible trade-offs between image quality and task performance without requiring task-specific training.