Guided Diffusion for the Extension of Machine Vision to Human Visual Perception
This addresses the need for efficient image compression that serves both AI tasks and human viewers, though it appears incremental by building on existing diffusion and ICM methods.
The paper tackles the problem of scalable image coding for both machine vision and human visual perception by proposing a method that uses guided diffusion to generate human-viewable images from machine vision outputs, achieving transitions without additional bitrate overhead and comparing compression performance in terms of bitrate and image quality.
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.