Deep　learning　and　convolutional　neural　networks　have　achieved　a　great　success　in　computer　vision　and　image　processing,　especially　in　low-level　vision　problems　such　as　image　compression.　Recently,　some　end-to-end　image　compression　methods　have　been　proposed　leading　to　a　new　direction　of　image　compression.　In　this　paper,　we　propose　an　end-to-end　reference　resource　based　image　compression　scheme　to　exploit　the　strong　correlations　with　external　similar　images.　In　the　proposed　scheme,　the　side　information　is　generated　from　highly　correlated　images　in　the　reference　resource.　The　features　of　side　information　can　conceptually　guide　the　compression　process　and　assist　the　reconstruction　process.　The　important　map　is　employed　to　guide　the　allocation　of　local　bit　rate　of　the　residual　features.　The　proposed　compression　scheme　is　formulated　as　a　rate　distortion　optimization　problem　in　an　end-to-end　manner　which　is　solved　by　ADAM　algorithm.　Experimental　results　prove　that　the　proposed　compression　framework　greatly　outperforms　several　image　compression　frameworks.