This paper discusses a model of combining multiple error factors based on multicriteria optimization. Frequently this involves fitting calculated errors to subjective data, and using the resulting scalar weighting as the single-valued error measure to optimize. Instead of finding a way of optimizing a fixed combination of the different factors, we consider the multiple error measures as a vector-valued function, thus producing data for an optimization problem with multiple objective functions. Applying multiple criteria optimization techniques to the resulting problem can yield a range of potentially optimal weightings for each factor. By adding a degree of freedom by way of the utility function, which describes how strongly each objective function contributes to the optimal weighting, we can remove the dependence on fixed scalar combinations resulting from fixed viewing conditions. Using this model, an end user or compression method designer can adaptively set their own preferred weighting. This paper discusses the relevant multiple criteria optimization theory, and describes our experiments with applying these techniques to the PQS model of Miyahara, Kotani and Algazi, applied to greyscale still images. We also describe how such methods could be generalized to models in which each error measure is described using entire images rather than single factors. These include Osberger's Region-of-Interest map, and Daly's Probability Detection Map.