An objective measurement framework for signal-level image fusion performance, based on a direct comparison of visual information in the fused and input images, is proposed. The aim is to model and predict subjective fusion performance results otherwise obtained through extremely time- and resource-consuming perceptual evaluation procedures. The measure associates visual information with edge, or gradient, information that is initially parametrized at all locations of the inputs and the fused image. A perceptual-information preservation model is then used to quantify the success of information fusion as the accuracy with which local gradient information is transferred from the inputs to the fused image. By considering the perceptual importance of different image regions, such local fusion success estimates are integrated into a single, numerical fusion performance score between 0 (total information loss) and 1 (ideal fusion). The proposed metric is optimized and validated using extensive subjective test results and validation procedures. The results clearly indicate that the proposed metric is perceptually meaningful in that it corresponds well with the results of perceptual fusion evaluation. Finally, an application of the proposed evaluation approach to fusion algorithm selection and fusion parameter optimization demonstrates its general usefulness.