A Scalable Image Compression Using Conditional Diffusion Model in Human-Machine Hybrid Vision

With the growing demand for machine analysis of images and videos, there is a strong need for new codecs that support both human and machine vision. However, existing scalable codecs suffer from redundancy between the enhancement and base layers, while scalable codecs for human-machine hybrids struggle to provide sufficiently high subjective image quality at low bit rates. To address these two issues, we propose a method to reduce redundancy by constraining the redundant information between the enhancement and base layer bit streams. We present a decoder based on a conditional diffusion model that significantly improves subjective image quality by enhancing the fidelity of human perception, especially at low bit rates. Our method enforces orthogonality during training and employs a channel-based entropy model to improve latent variable distribution prediction. Experimental results show that our method achieves up to 81.96% gain in bit rate and subjective image quality comparable to current state-of-the-art techniques in machine vision tasks.