For evaluating and improving image compression algorithms, there is a great need for an image fidelity metric that measures the perceptual difference between images. Image discrimination models, that model for human visual system, have been suggested as such metrics. The models found in the literature vary considerably in features, complexity and performance. The suitability of a certain model will depend on the application. Trying to find a model suitable for a particular application is difficult since most models are reported for different applications and supported by different data. Furthermore, tuning a particular model to a new application and environment is not a straightforward exercise. In this paper, we have brought together some well- known image discrimination models and compared their performance against one set of psychophysical data, with image fidelity as the intended task. The data was collected for three types of distortion: blocking, blurring and ringing. A comparison of these results from the different models showed that models using cross-channel masking gave the best overall results. However, the difference in performance for different models was small and the performance vary for the different types of distortion, but all models were better than a traditional metric (Peak Signal to Noise Ratio).