In embedded systems, many numerical algorithms are implemented with fixed-point arithmetic to meet area cost and power constraints. Fixed-point encoding decisions can significantly affect cost and performance. To evaluate their impact on accuracy, designers resort to simulations. Their high running-time prevents thorough exploration of the design-space. To address this issue, analytical modeling techniques have been proposed, but their applicability is limited by scalability issues. In this paper, we extend these techniques to a larger class of programs. We use polyhedral methods to extract a more compact, graph-based representation of the program. We validate our approach with a several image and signal processing algorithms.