In　this　paper,　a　natural　image　compression　method　is　proposed　based　on　independent　component　analysis　(ICA)　and　visual　saliency　detection.　The　proposed　compression　method　learns　basis　functions　trained　from　data　using　ICA　to　transform　the　image　at　first;　and　then　sets　percentage　of　the　zero　coefficient　number　in　the　total　transforming　coefficients.　After　that,　transforming　coefficients　are　sparser　which　indicates　further　improving　of　compression　ratio.　Next,　the　compression　method　performance　is　compared　with　the　discrete　cosine　transform　(DCT).　Evaluation　through　both　the　usual　PSNR　and　Structural　Similarity　Index　(SSIM)　measurements　showed　that　proposed　compression　method　is　more　robust　to　DCT.　And　finally,　we　proposed　a　visual　saliency　detection　method　to　detect　automatically　the　important　region　of　image　which　is　not　or　low　compressed　while　the　other　regions　are　highly　compressed.　Experiment　shows　that　the　method　can　guarantee　the　quality　of　important　region　effectively.