Applications of a spatial extension to CIELAB

We describe computational experiments to predict the perceived quality of multi-level halftone images. Our corn-putations were based on a spatial color difference metric, S-CIELAB, that is an extension of CIELAB, a widely used industry standard. CIELAB predicts the discriminability of large uniform color patches. S-CIELAB includes a pre-processing stage that accounts for certain aspects of the spatial sensitivity to different colors. From simulations applied to multi-level halftone images, we found that (a) for grayscale images, L*spacing of the halftone levels results in better halftone quality than linear-spacing of the levels; (b) for color images, increasing the number of halftone levels for magenta ink results in the most significant improvement in halftone quality. Increasing the number of halftone levels of the yellow ink resulted in the least improvement. 1