Recent　advances　in　Learned　Image　Compression　(LIC)　utilize　the　powerful　Masked　Image　Modeling　(MIM)　framework　(e.g.,　MAE).　However,　the　random　masking　treats　all　patches　equally,　which　could　lead　(i)　missing　critical　information　and　(ii)　poor　robustness,　since　different　patches　may　have　different　contributions　to　image　reconstruction.　The　principle　experiment　shows　that　the　patch-level　inherent　feature　evaluation　is　highly　correlated　with　the　quality　of　the　reconstructed　image.　Based　on　this　key　insight,　we　propose　a　simple　mask　selection　approach　based　on　the　patch’s　inherent　feature.　Specifically,　we　design　plug-and-play　frequency　and　spatial　inherent　feature　modules　to　select　masks,　enhancing　various　MIMs　based　on　the　MAE　model　and　achieving　high-quality　image　reconstruction　and　strong　robustness　at　high　mask　rates　for　data-intensive　compression　tasks.　©　2024　IEEE.