Intensity based image registration is one of the most popularly used methods for automatic image registration. In the recent past, various improvements have been suggested, ranging from variation in the similarity metrics (Correlation Ratio, Mutual Information, etc.) to improvement in the interpolation techniques. The performance of one method over the other is observed either from the final results of registration or visual presence of artifacts in the plots of the objective function (similarity metric) vs. the transformation parameters. None of these are standard representations of the quality of improvement. The final results are not indicative of the effect of the suggested improvement as it depends on various other components of the registration process. Also visual assessment of the presence of artifacts is feasible only when the number of parameters in the transformation involved are less than or equal to two. In this paper, we introduce a novel approach and a metric to quantify the presence of artifacts, which in turn determines the performance of the registration algorithm. This metric is based on the quality of objective-function landscape. Unlike, the already existing methods of comparison, this metric provides a quantitative measure that can be used to rank different algorithms. In this paper, we compare and rank different interpolation techniques based on this metric. Our experimental results show that the relative ordering provided by the metric is consistent with the observation made by traditional approaches like visual interpretation of the similarity metric plot. We also compare and compute the proposed metric for different variants of intensity-based registration methods.