# Guided Diffusion for the Extension of Machine Vision to Human Visual Perception arXiv: https://arxiv.org/abs/2503.17907 ยท HTML: https://mlsanity.com/paper/2503.17907/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. - Builds on 33 of its 45 references that are in the map. - First posted on arXiv 23 Mar 2025. ## Placed in - Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/) ## Builds on (most cited first, 30 of 33) - [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) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018) - [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018) - [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016) - [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018) - [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020) - [Learned Image Compression with Mixed Transformer-CNN Architectures](https://mlsanity.com/paper/2303.14978/) (2023) - [Text + Sketch: Image Compression at Ultra Low Rates](https://mlsanity.com/paper/2307.01944/) (2023) - [Scalable Image Coding for Humans and Machines](https://mlsanity.com/paper/2107.08373/) (2021) - [Image coding for machines: an end-to-end learned approach](https://mlsanity.com/paper/2108.09993/) (2021) - [Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression](https://mlsanity.com/paper/2004.10043/) (2020) - [Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation](https://mlsanity.com/paper/2407.09853/) (2024) - [High efficiency compression for object detection](https://mlsanity.com/paper/1710.11151/) (2017) - [Visual Analysis Motivated Rate-Distortion Model for Image Coding](https://mlsanity.com/paper/2104.10315/) (2021) - [Learned Image Coding for Machines: A Content-Adaptive Approach](https://mlsanity.com/paper/2108.09992/) (2021) - [Image Coding for Machines with Edge Information Learning Using Segment Anything](https://mlsanity.com/paper/2403.04173/) (2024) - [Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2111.02249/) (2021) - [End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization](https://mlsanity.com/paper/2107.00328/) (2021) - [ROI-based Deep Image Compression with Swin Transformers](https://mlsanity.com/paper/2305.07783/) (2023) - [Scalable Video Coding for Humans and Machines](https://mlsanity.com/paper/2208.02512/) (2022) - [Image Coding for Machines with Object Region Learning](https://mlsanity.com/paper/2308.13984/) (2023) - [Scalable Image Coding for Humans and Machines Using Feature Fusion Network](https://mlsanity.com/paper/2405.09152/) (2024) - [Learned Scalable Video Coding For Humans and Machines](https://mlsanity.com/paper/2307.08978/) (2023) - [Accuracy Improvement of Object Detection in VVC Coded Video Using YOLO-v7 Features](https://mlsanity.com/paper/2304.00689/) (2023) - [Refining Coded Image in Human Vision Layer Using CNN-Based Post-Processing](https://mlsanity.com/paper/2405.11894/) (2024) ## Built on by (most cited first, 2 of 2) - [Progressive Learned Image Compression for Machine Perception](https://mlsanity.com/paper/2512.20070/) (2025) - [Seed Selection for Human-Oriented Image Reconstruction via Guided Diffusion](https://mlsanity.com/paper/2506.05363/) (2025) ## Abstract (arXiv) Image compression technology eliminates redundant information to enable efficient transmission and storage of images, serving both machine vision and human visual perception. For years, image coding focused on human perception has been well-studied, leading to the development of various image compression standards. On the other hand, with the rapid advancements in image recognition models, image compression for AI tasks, known as Image Coding for Machines (ICM), has gained significant importance. Therefore, scalable image coding techniques that address the needs of both machines and humans have become a key area of interest. Additionally, there is increasing demand for research applying the diffusion model, which can generate human-viewable images from a small amount of data to image compression methods for human vision. Image compression methods that use diffusion models can partially reconstruct the target image by guiding the generation process with a small amount of conditioning information. Inspired by the diffusion model's potential, we propose a method for extending machine vision to human visual perception using guided diffusion. Utilizing the diffusion model guided by the output of the ICM method, we generate images for human perception from random noise. Guided diffusion acts as a bridge between machine vision and human vision, enabling transitions between them without any additional bitrate overhead. The generated images then evaluated based on bitrate and image quality, and we compare their compression performance with other scalable image coding methods for humans and machines.