In the superpixel literature, the comparison of state-of-the-art methods can be biased by the nonrobustness of some metrics to decomposition aspects, such as the superpixel scale. Moreover, most recent decomposition methods allow setting a shape regularity parameter, which can have a substantial impact on the measured performances. We introduce an evaluation framework that aims to unify the comparison process of superpixel methods. We investigate the limitations of existing metrics and propose to evaluate each of the three core decomposition aspects: color homogeneity, respect of image objects, and shape regularity. To measure the regularity aspect, we propose a global regularity (GR) measure, which addresses the nonrobustness of state-of-the-art metrics. We evaluate recent superpixel methods with these criteria, at several superpixel scales and regularity levels. The proposed framework reduces the bias in the comparison process of state-of-the-art superpixel methods. Finally, we demonstrate that the proposed GR measure is correlated with the performances of various applications.