Recent advancements in digital imaging systems have provided means for greater definition in photography. These technologies have brought high-resolution pictures that show greater clarity and allow for a 'deeper' analysis of information. This occurs through decreased distortion that may have otherwise resulted in features becoming 'blurred' or 'hidden'. However, this strength is accompanied by a weakness due to the large amount of data needed to display the otherwise non-apparent information; in turn creating massive file sizes which produce troubling or infeasible computation times during image storage and transfer. Therefore methods for handling these giant image files could be of great use in image processing and compression if an adequate amount of information can be maintained. During analysis the degree to which information must be replicated exactly depends on the purpose of the final product and the capability of computational resources. This work uses Graph Based Evolutionary Algorithms (GBEAs) to segment images into balanced-weight Voronoi tessellations which are then modeled by least squares surface fitting techniques using the Adaptive Modeling by Evolving Blocks Algorithm (AMoEBA). This twice optimized process yields new techniques of preserving image quality during image analysis and complex decision making. Methods in this paper begin by breaking an image into numerous components using balanced-weight Voronoi tessellations that are optimized to conform to features and detail in an image. These features are represented by surfaces through the strength and efficiency of the AMoEBA algorithm to find optimal representations that can differ from one tessellation to another.