In　recent　years,　there　has　been　a　substantial　increase　in　the　amount　of　visual　data　generated　by　edge　devices.　Machines　typically　process　this　data　to　accomplish　tasks　such　as　object　detection　without　human　visual　judgment.　However,　human　viewing　is　sometimes　required　during　human-robot　interaction.　Here,　there　exists　a　significant　difference　in　the　focus　of　information　between　humans　and　machines.　To　tackle　this　issue,　we　propose　an　end-to-end　learning-based　image　coding　framework,　aiming　to　strike　a　balance　between　human　and　machine　vision　tasks.　Also,　a　portion　of　the　latent　space　is　used　for　both　machine　vision　and　human　vision.　This　is　different　from　a　compression　framework　that　only　targets　human　vision.　Because　of　this　difference,　correlations　still　exist　between　tasks.　So　we　propose　a　partial-channel　context　model　to　improve　coding　performance.Our　scalable　coding　framework　achieves　simultaneous　support　for　both　human　and　machine　vision　by　partitioning　the　latent　space.　Machine　vision　tasks　are　handled　by　a　subset　of　the　latent　space,　referred　to　as　the　base　layer.　More　complex　human　visual　reconstruction　tasks　are　accomplished　by　an　additional　subset　of　the　latent　space,　comprising　both　base　and　enhancement　layers.　In　the　experimental　section,　we　present　the　performance　of　human　visual　reconstruction　and　machine　vision　tasks,　comparing　them　with　other　benchmarks.　The　experiments　demonstrate　that　our　framework　achieves　a　28.27%-38.16%　reduction　in　bitrate　for　machine　vision　tasks　and　matches　the　performance　of　state-of-the-art　image　codecs　in　terms　of　input　reconstruction.　©　2024　IEEE.