Image compression for flow visualization and analysis

摘要

Pattern models for the analysis, visualization, and compression of experimental 2-D flow imagery are developed. Linear and nonlinear models are presented, both of which use the linear phase portrait as a basic building block. These techniques require orientation field computation, critical point detection, and estimation of the associated phase portraits as preliminary analysis steps. In the linear case flows are modeled as a superposition of phase portraits, where their strengths are determined from the orientation field. This works well for flows that exhibit nearly ideal behavior, and a modification is included which is applicable to a wider range of flows. In the nonlinear case flows are modeled by differential equations of Taylor series form. Inclusion of higher order nonlinear terms provides for better modeling of non-ideal flows. The nonlinear coefficients are computed from the estimated linear phase portrait descriptions. The output of these modeling techniques is a compact set of coefficients from which the original flow streamlines are visualized. Finally, the derived models are employed to compress scalar images that exhibit little or gradual variation along the flow streamlines. Compression ratios on the order of 100:1 are achieved.