Content created in High Dynamic Range (HDR) and Wide Color Gamut (WCG) is becoming more ubiquitous, driving the need for reliable tools for evaluating the quality across the imaging ecosystem. One of the simplest techniques to measure the quality of any video system is to measure the color errors. The traditional color difference metrics such as ΔE₀₀ and the newer HDR specific metrics such as ΔE_Z and ΔE_ITP compute color difference on a pixel-by-pixel basis which do not account for the spatial effects (optical) and active processing (neural) done by the human visual system. In this work, we improve upon the per-pixel ΔE_ITP color difference metric by performing a spatial extension similar to what was done during the design of S-CIELAB. We quantified the performance using four standard evaluation procedures on four publicly available HDR and WCG image databases and found that the proposed metric results in a marked improvement with subjective scores over existing per-pixel color difference metrics.