Recently,　the　deep　learning　methods　have　been　widely　used　in　lossy　compression　schemes,　greatly　improving　image　compression　performance.　In　this　paper,　we　propose　an　extended　hybrid　image　compression　scheme　based　on　soft-to-hard　quantification,　which　has　only　two　layers.　The　compact　representation　of　the　input　image　is　encoded　by　the　FLIF　codec　as　the　base　layer.　The　residual　of　the　input　image　and　the　reconstructed　image　is　encoded　by　the　BPG　codec　as　the　enhancement　layer.　The　results　using　the　Kodak　and　Tecnick　datasets　show　that　the　performance　of　our　proposed　methods　exceeds　some　image　compression　schemes　based　on　deep　learning　methods　and　some　traditional　coding　standards　including　BPG　in　SSIM　metric　across　a　wide　range　of　bit　rates,　when　the　images　are　coded　in　the　RGB444　domain.　We　explore　the　issue　of　bit　rates　allocation　of　the　base　layer　and　enhancement　layer　and　the　impact　of　enhancement　layer　codecs.　Also,　we　analyze　the　limitations　of　the　hybrid　coding　scheme.　©　2013　IEEE.