The goal of this work is to present and review two new image difference metrics, named S_DOG – CIELAB and S_DOG – DEE. These metrics are along the same lines as the standard SCIELAB metric (Zhang and Wandell, 1997), modified to include a pyramidal subsampling, the Difference of Gaussians receptivefield model (DOG) (Tadmor and Tolhurst, 2000), and the ΔE_E color-difference formula (Oleari et al., 2009). The DOG model and the ΔE_E formula have been shown to improve respectively contrast measures and image quality metrics (Simone et al., 2009). Extensive testing using 29 state-of-the-art metrics and six image databases has been performed. Although this new approach is promising, we only find weak evidence of effectiveness. Analysis of the results indicates that the metrics show fairly good correlations over particular test images, yet they do not outperform the most common objective quality measures.