No abstract

The aim of this study was to use a minimum number of measured colour patches to evaluate the colour gamut of an n-colour printing process. Traditionally, the colour gamut of a printing system has been derived by printing and then measuring a gamut target for example, a profiling chart. For an n-colour printing (printing with more than four process inks), it is desirable to know the colour gamut of the given set of inks without having to print a large number of test patches. Different spectral printer models were used to predict the gamut of a 7-colour printing process. The colorant space was divided into sectors each containing four inks. For each printer model, the colour gamut of the each four-ink sector was predicted. All sector-gamuts were then combined to predict the overall colour gamut of the n-colour process. This predicted gamut was then compared with the gamut obtained by measurement using a gamut comparison index (GCI). The Yule-Nielsen modified spectral Neugebauer (YNSN) model gave the best accuracy, at the cost of a larger number of input measurements, than other models. A combination of the Kubelka-Munk (KM) and YNSN models performed well with the fewest input measurements.

No abstract

No abstract

In printing industry, it is often necessary to be able to compare two or more colour gamuts, to determine how similar they are. For example, if a colour image is to be retargeted from one output medium to a different medium, we may wish to know whether the second has a sufficiently similar gamut to make an acceptable reproduction a possibility. The gamut volume alone enables a comparison of the size of the gamut, but not whether the gamuts intersect sufficiently to meet the reproduction aims. This can be achieved by visual comparison of the two gamut volumes in a pseudo-3D rendering, but it is also useful to have a single-number value, which enables this comparison to be computed from the gamut boundary description of the two gamuts. CIE TC8-05 defined metrics for evaluating single colour gamuts and these metrics can be used to make some comparisons between gamuts. However, they do not give information on how well one gamut can match another in terms of both intersection and volume. In this work, a number of metrics are proposed for comparing and analyzing two colour gamuts. The primary metric is the Gamut Comparison Index (GCI), which quantifies the similarity between gamuts. A number of other related metrics are also defined, and several test cases for the metrics are computed. The results indicate that the metrics give a consistent prediction of how well the two gamuts compared. Use of the resulting GCI value should enable users to select the transform or device that best matches the reproduction aim. It should also help vendors and users evaluate the gamuts of their devices against the appropriate standard colour gamuts.