Metrics for comparing and analyzing two colour gamuts

One of the most important subjects of digital printing is evaluation of color gamut mapping. A printer can only reproduce colors that are in its color gamut. Maybe, a color tolerance between reference sample and printed sample is in agreement between manufacturer and customer. The goal of this work is to estimate the expansion of the color gamut of an inkjet textile printer by consideration of the acceptable (predetermined) color difference during color matching. For analyzing the color gamut, a standard color chart IT8.7/4‐2005 CMYK target was printed by nine different mixtures of CMYK, and then the reflectance spectra and colorimetric parameters of the printed samples were measured. The inks used were disperse water‐based inks that were printed on the polyester fabric. The maximum acceptable color difference in this study was equal to 2 CIELAB units for all colors. In order to estimate the color gamut with the maximum acceptable color difference, for all boundary points of gamut, expanded points were obtained by Euclidean distance less than ∆Eab* = 2. The results show that when the acceptable color difference is considered in color gamut measuring, the area and volume of the color gamut are increased and so, the reproducibility of printer is increased. The maximum and minimum expansions of color gamut volumes were obtained by the mixtures with minimum and maximum color gamuts, respectively. The volume expansion of the mixture with the minimum color gamut volume was 34.20%, while the volume expansion of the mixture with the maximum color gamut volume was 28.06%.

Section: Introductionmentioning

confidence: 99%

Expanding the color gamut of inkjet textile printing during color matching

Hajipour

Shams‐Nateri

2021

“…due to the lack of consideration of the constructed gamut volume. In the method proposed by Deshpande et al ., gamut similarity is evaluated by gamut comparison index (GCI); however, GCI cannot quantify the real difference between two gamuts, and it is impossible to know whether one gamut is overestimated or underestimated by another gamut which was constructed with a different GBD algorithm. In their evaluation method, the reference gamut is constructed by convex hull or alpha shape algorithm, which makes it impossible to be used in the evaluation of different GBD algorithms, because this would not be fair for other GBD algorithms.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

A new device color gamut boundary description algorithm based on irregular segmentation

Zhang

Jiang

Mei

2016

The description of device color gamut is very important for color gamut mapping, which plays an important role in cross‐media color reproduction. To get a more accurate color gamut boundary, several gamut boundary description (GBD) methods have been presented in the past; however, many problems still exist in the current algorithm (e.g., accuracy and speed). In this study, we propose a new device GBD method based on irregular segmentation and evaluate it against other GBD algorithms. In the irregular segmentation method, the device color gamut is divided into the high‐chroma and the low‐chroma color parts, and the high‐chroma parts are divided into more segments through the CIELAB a*b* plane when calculating color gamut descriptors. After that, in each segment, the radii between the color points and the center point are calculated. For the color points located in the high‐chroma parts, the color point with the biggest radius is selected as the gamut boundary descriptor, and for that in the low‐chroma parts, if the corresponding outer segments are empty, the color point with the biggest radius is selected as the gamut boundary descriptor. Then, the gamut boundary descriptors are visualized in CIELAB color space using triangulation method. Finally, the irregular segmentation method is evaluated in contrast with other GBD methods using gamut mismatch index. The results show that the irregular segmentation method performs better in accuracy and has smaller variance. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 173–181, 2017

“…The gamut volume alone enables a comparison of the size of the gamut, but not whether the gamuts intersect sufficiently to have a good match. To address this issue, the Gamut Comparison Index (GCI) metric, which includes both relative volume and intersection, was used.…”

Section: Resultsmentioning

confidence: 99%

Gamut evaluation of an n‐colour printing process with the minimum number of measurements

Deshpande

Green

Pointer

2014

Self Cite

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.