Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework

The recent publication titled 'Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Framework' presents a groundbreaking approach to data compression that emphasizes machine vision. Traditional methods primarily focus on human vision, often leading to inefficiencies in data handling. This new framework proposes a collaborative compression method that leverages machine vision's ability to focus on essential image regions, thereby reducing the amount of data needed for effective human perception. The research introduces a plug-and-play variable bit-rate strategy tailored for machine vision tasks and demonstrates the effectiveness of the Diffusion-prior based feature compression method (Diff-FCHM). Experimental results indicate that Diff-FCHM outperforms existing techniques in both machine and human vision compression, marking a significant advancement in the field of human-machine collaboration in data processing.