Abstract: Traditional automatic color optimization methods face the challenge of expressing global features in dynamic data ranges.To solve this problem,an adaptive colormap optimization method based on inserting col-ors is proposed,which includes a process of estimating color inserting position and an inserting color optimi-zation procedure.Firstly,color inserting positions are selected based on color discriminability and data histo-gram distribution.By keeping the color inserting positions,corresponding embedding colors are estimated through a novel energy optimization equation under the guidance of visual discriminability and the consistency to the original colormap.On the basis of the algorithm,an interactive visual data exploratory system is pro-vided,which includes supporting global data perception and local ROI analysis.The effectiveness and applica-bility of the algorithm is evaluated via a user study and a case study,based on 6 colormaps with different color features and 8 datasets with different data distributions.The results demonstrate that proposed method can produce high quality visual data information compared with other algorithms,providing a condition for further data analysis.