Method and system for color formulation

Abstract

A color formulation system (100) receives spectral target data (111) of a target sample (110), the spectral target data (111) being acquired under a first set of measurement conditions. The database includes optical data (104) associated with a second set of measurement conditions. A conversion engine (101) receives the spectral target data and converts them into converted target data (112) representing an expected spectral response of the target sample under a second set of measurement conditions. The formulation engine uses the converted target data and optical data in the database to predict a recipe of the candidate material (140). In other embodiments, a conversion engine receives spectral calibration data associated with a second set of measurement conditions and converts them into converted calibration data associated with a first set of measurement conditions, which is then used to determine optical data in a database.

Description

Technical Field

[0001] The present invention relates to a method for color formulation and a color formulation system configured to perform the method. Background Art

[0002] Finding a color formulation that matches a desired target color can be a long and tedious process. Traditionally, color formulation has been a completely manual process. Success depends largely on the experience of the color professional who selects the colorants and defines the candidate formulations. Even experienced experts often need several iterations until a satisfactory match is obtained.

[0003] In the past few decades, increasingly sophisticated color formulation software has been introduced to help color professionals obtain color formulations within defined tolerances with fewer iterations. Target data representing the spectral response of a target sample is provided to the formulation software, and the formulation software proposes one or more formulations for candidate materials expected to match the appearance of the target sample. The core part of the color formulation software is the so-called "formulation engine". This is a set of algorithms that can predict the reflectance or transmittance of a mixture of certain components (referred to as a "formulation" or "recipe") and create or correct a formulation to match a given target reflectance or transmittance. The central part of the engine simulates the interaction of light with a mixture of certain components. For this purpose, the engine requires optical data that describes the light transmission properties of the individual components. The optical data is generated from calibration data that has been obtained by measuring specially prepared calibration samples that contain the component as well as a binder and at most very few other additional materials.

[0004] A suitable spectrophotometer is used to determine the calibration data and the target data. There are many different spectrophotometers with light sources and light detectors of different geometries and with different types of light sources. The most suitable type of spectrophotometer usually depends on the type of material and the actual use.

[0005] For example, automotive paint typically exhibits gonioapparent behavior, i.e., the appearance of automotive paint usually depends on the angle of illumination and observation. Therefore, for formulating automotive paint, a multi-angle spectrophotometer is usually used, which determines the spectral information for multiple combinations of well-defined illumination and observation directions.

[0006] As another example, for measuring wall paints and other typical retail paints that do not have angular appearance properties, an integrating sphere spectrophotometer is commonly used. The integrating sphere spectrophotometer includes a hollow spherical cavity defined by a diffusely reflecting white inner surface, having a measurement port where the sample is placed, at least one inlet port for illumination, and at least one outlet port for observation. The integrating sphere results in a uniform scattering or diffusing effect. Through multiple scattering reflections, light incident on any point on the inner surface is evenly distributed to all other points. The influence of the original direction of the light is minimized. The outlet port is typically arranged at an 8° angle to the surface normal of the sample. The resulting geometry will be referred to hereinafter as "D / 8". A gloss trap can be arranged in the specular direction of the outlet port to exclude the specular contribution to the total reflectance. In this case, the geometry will be denoted hereinafter as "specular excluded" or simply "spex". If no gloss trap is present, the geometry will be denoted as "specular included" or "spin".

[0007] As yet another example, in some less complex usage scenarios, a spectrophotometer having only a single illumination direction and a single observation direction is used. The illumination direction is typically at a 45° angle to the surface normal, while the observation direction is typically at a 0° angle to the surface normal. Such an instrument is called a 45 / 0 spectrophotometer. Sometimes the illumination source forms a ring, thus illuminating the measurement point at 45° in a complete circle. In other examples, three or more discrete light sources are distributed on the circle.

[0008] Each formulation engine requires spectral target data that has been acquired under specific measurement conditions (measurement geometry and illumination characteristics). For example, a formulation engine designed to receive multi-angle target data typically cannot process D / 8 target data, and vice versa. Each formulation engine further requires that the optical data has been generated from calibration data determined using the same measurement conditions as the target data. In practice, this means that a spectrophotometer of the same type as the calibration data is needed to determine the target data. Thus, each formulation engine is bound to a specific type of spectrophotometer.

[0009] However, it may happen that a user in the field only has access to a spectrophotometer of a different type than the one to which the formulation engine is bound. For example, the user may only have access to a multi-angle spectrophotometer, while the formulation engine requires D / 8 data from an integrating sphere spectrophotometer. In such a case, it has not been possible to use the formulation engine to date.

[0010] The fact that the formulation engine is tied to specific measurement conditions also hinders innovation in the field of color formulation. If a new and potentially better type of spectrophotometer is introduced into the market, a new formulation engine must be developed and the calibration measurements must be completely redone with this new type of spectrophotometer. For a large colorant library, this can be an extremely time-consuming and costly task. This obstacle may hinder the effort to introduce new and potentially better spectrophotometers into environments using formulation software. Summary of the Invention

[0011] An object of the present invention is to provide a color formulation system that enables the use of a formulation engine even if the formulation engine does not support the measurement conditions under which the target data or calibration data is obtained. Specifically, an object of the present invention is to provide a system for color formulation that can be used even if the target data has been obtained by an instrument of a different type from the calibration data.

[0012] This object is achieved by the color formulation system according to claim 1 or 3. Further embodiments of the present invention are listed in the dependent claims. A corresponding method is also disclosed.

[0013] In a first aspect, the present invention provides a color formulation system, comprising a conversion engine, a formulation engine, and a first database,

[0014] wherein the color formulation system is configured to receive spectral target data, the spectral target data representing the spectral response of a target sample under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic,

[0015] wherein the first database includes optical data, the optical data representing the light transmission characteristics of a plurality of colorants under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, and at least one measurement condition in the second set is different from at least one measurement condition in the first set,

[0016] wherein the conversion engine is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being the spectral target data, and the converted spectral data being converted target data representing the predicted spectral response of the target sample under the second set of measurement conditions, and

[0017] wherein the formulation engine is configured to use the converted target data and the optical data in the first database to predict a formulation of a candidate material that is expected to match the appearance of the target sample.

[0018] According to a first aspect, spectral target data is converted into converted target data. The converted target data represents the predicted spectral response of a target sample under measurement conditions different from those under which the target data was actually obtained. The converted target data is then used by a formulation engine. In this way, the spectral target data can be used for formulation even if the formulation engine does not support the measured spectral target data, or if the optical data used by the formulation engine is associated with measurement conditions different from those under which the target data was obtained.

[0019] The color formulation system may further include a calibration engine for generating optical data from calibration data. Specifically, the color formulation system may include: a second database including spectral calibration data representing the spectral responses of a plurality of calibration samples under a second set of measurement conditions; and a calibration engine configured to calculate the optical data based on the calibration data in the second database and store the calculated optical data in the first database. Suitable calibration engines are well known in the art.

[0020] In a second aspect, the present invention provides a color formulation system including a conversion engine, a formulation engine, a calibration engine, a first database, and a second database,

[0021] wherein the color formulation system is configured to receive spectral target data representing the spectral response of a target sample under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic,

[0022] wherein the second database includes spectral calibration data representing the spectral responses of a plurality of calibration samples under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, with at least one measurement condition in the second set being different from at least one measurement condition in the first set,

[0023] wherein the conversion engine is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being the spectral calibration data in the second database, and the converted spectral data being converted calibration data representing the expected spectral response of the calibration samples under the first set of measurement conditions,

[0024] wherein the calibration engine is configured to calculate optical data based on the converted calibration data and store the calculated optical data in the first database, the calculated optical data representing the light transmission characteristics of a plurality of colorants under the first set of measurement conditions, and

[0025] Wherein the formulation engine is configured to use spectral target data and optical data in a first database to predict a formulation of a candidate material that is expected to match the appearance of a target sample.

[0026] According to a second aspect, calibration data is converted into transformed calibration data, and optical data is generated from the transformed calibration data. This enables the use of calibration data that is measured using a particular type of spectrophotometer with a formulation engine developed for different types of spectrophotometers. Thus, when a new and potentially better formulation engine has been developed, an existing database of calibration data can be reused without having to repeat all of the calibration measurements from scratch.

[0027] The color formulation system can further include a third database that includes additional optical data that has been obtained from calibration measurements of a calibration sample under a first set of measurement conditions, and the formulation engine can additionally use the third database to predict a formulation. In this way, optical data obtained through calibration measurements under both the first and second sets of measurement conditions can be used. In particular, "traditional" calibration data obtained under a second set of measurement conditions for which an "old" formulation engine is available can be augmented with "new" calibration data obtained under a first set of measurement conditions required by the "new" formulation engine.

[0028] The color formulation system can include a plurality of formulation engines that can alternatively be used to predict a formulation. The formulation system can then include a user interface ("digital agent") that is configured to prompt the user to select from among the plurality of formulation engines to select the formulation engine to be used to predict a formulation.

[0029] The user interface can execute on a physical device different from the formulation engine. In particular, the color formulation system can be cloud-based, where different components of the color formulation system are implemented on different physical devices that can be remote from each other and can be connected via a network, particularly the Internet.

[0030] In some embodiments, the user interface can be configured to perform one or more of the following steps:

[0031] a) Prompt the user to provide information about the type of material to which the spectral target data pertains, such as wall paint or car paint. This can be done by presenting the user with a list of possible material types and prompting the user to select from the list.

[0032] b) Prompt the user to provide information for identifying the first set of measurement conditions. This can be done by presenting a list to the user and prompting the user to make a selection from the list. For example, a list such as the brand and model of the measurement device, such as a spectrophotometer, can be presented to the user, since selecting a particular brand and model of the measurement device can automatically define the measurement conditions.

[0033] c) Prompt the user to specify a formulation engine, for example by presenting a list of suitable formulation engines to the user and prompting the user to make a selection from the list.

[0034] d) Optionally, determine whether a conversion is required for using the formulation engine and output information related to the need for conversion to the user.

[0035] Instead of step d), the user interface can be configured to present a list of suitable formulation engines to the user and information on whether a conversion is required for each of the formulation engines.

[0036] In a third aspect, the present invention provides a system for determining optical data to be used in color formulation, the optical data representing the light transmission characteristics of a plurality of colorants under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, the system comprising:

[0037] A conversion engine, a calibration engine, a first database, and a second database,

[0038] wherein the second database includes spectral calibration data representing the spectral responses of a plurality of calibration samples under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, and at least one of the measurement conditions in the second set is different from at least one of the measurement conditions in the first set,

[0039] wherein the conversion engine is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being the spectral calibration data in the second database, and the converted spectral data being converted calibration data representing the predicted spectral responses of the calibration samples under the first set of measurement conditions, and

[0040] wherein the calibration engine is configured to calculate the optical data based on the converted calibration data and store the calculated optical data in the first database, the calculated optical data representing the light transmission characteristics of a plurality of colorants under the first set of measurement conditions.

[0041] Thus, the system of the third aspect uses a concept similar to that of the system of the second aspect of the present invention to calculate and store the optical data to be used by any suitable formulation engine.

[0042] The conversion engine can be configured to receive material information and take the material information into account when performing the conversion. The material information can include at least one of the following information items:

[0043] - Information about the type of colorant associated with the spectral input data;

[0044] - Information about the binder of one or more materials associated with the spectral input data;

[0045] - Information about the surface properties of the material associated with the spectral input data;

[0046] - Information about the refractive properties of the material associated with the spectral input data;

[0047] - Information about the scattering properties of the material associated with the spectral input data;

[0048] - Information about the fluorescence properties of the material associated with the spectral input data;

[0049] - Information about the polarization properties of the material associated with the spectral input data;

[0050] - Information about the substrate on which the material associated with the spectral input data has been applied; and

[0051] - Information about one or more pre-existing formulations that produce an approximate match to the appearance of the target sample.

[0052] The material information can be used not only by the conversion engine but also by the formulation engine. In particular, when predicting a formulation expected to match the appearance of the target sample, the formulation engine can use information about one or more pre-existing formulations to effectively "correct" the pre-existing formulations.

[0053] In some embodiments, the first and second measurement geometries are integrating sphere geometries, one of the integrating sphere geometries being mirror-including and the other being mirror-excluding. Then, the conversion can include calculating a refraction term and adding the refraction term to the spectral input data or subtracting the refraction term from the spectral input data, the refraction term representing the percentage of light reflected in the mirror direction.

[0054] In some embodiments, the conversion engine is configured to perform the following steps:

[0055] Based on the spectral input data, determine at least one parameter of a model of light transport, particularly a BRDF model;

[0056] Use the model of light transport to calculate the converted spectral data.

[0057] It is particularly useful to use the BRDF model if the measurement geometry associated with the spectral input data is a first multi - angular geometry and the measurement geometry associated with the transformed spectral data is a second multi - angular geometry, a fixed - angle geometry, in particular a 45 / 0 geometry or an integrating - sphere geometry. In such a case, the transformed spectral data can be easily calculated by evaluating the BRDF under the appropriate measurement geometry or, in the case of an integrating - sphere geometry, by integrating the BRDF over the hemisphere.

[0058] In some embodiments, one of the first and second measurement geometries is an integrating - sphere geometry and the other is a fixed - angle geometry, in particular a 45 / 0 geometry. The transformation includes calculating an integral over the percentage of light that can enter and leave the material associated with the spectral input data, the integral being performed over the hemisphere, and associating the integral with the percentage of light that can enter and leave the material under the fixed - angle geometry.

[0059] A computer - implemented method for color formulation corresponding to the first aspect of the present invention includes:

[0060] Receiving spectral target data that represents the spectral response of a target sample under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic,

[0061] Retrieving from a first database optical data that represents the light - transmission characteristics of a plurality of colorants under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, and at least one measurement condition in the second set being different from at least one measurement condition in the first set;

[0062] Performing a transformation of spectral input data into transformed spectral data, where the spectral input data is the spectral target data and the transformed spectral data is transformed target data that represents the expected spectral response of the target sample under the second set of measurement conditions, and

[0063] Using the transformed target data and the optical data in the first database to predict the formulation of a candidate material that is expected to match the appearance of the target sample.

[0064] A computer - implemented method for color formulation corresponding to the second aspect of the present invention, the method including:

[0065] Receiving spectral target data that represents the spectral response of a target sample under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic,

[0066] Retrieve spectral calibration data from a second database, the spectral calibration data representing the spectral responses of a plurality of calibration samples under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, and at least one measurement condition in the second set being different from at least one measurement condition in the first set;

[0067] Perform a conversion of spectral input data into converted spectral data, the spectral input data being the spectral calibration data, and the converted spectral data being converted calibration data representing the expected spectral responses of the calibration samples under the first set of measurement conditions,

[0068] Calculate optical data based on the converted calibration data and store the calculated optical data in a first database, the calculated optical data representing the light transmission characteristics of a plurality of colorants under the first set of measurement conditions, and

[0069] Use spectral target data and the optical data in the first database to predict the formulation of a candidate material that is expected to match the appearance of a target sample.

[0070] These methods may or may not include the step of determining spectral target data by measurement. In particular, the method may include at least one of the following:

[0071] Perform at least one measurement on a target sample using a spectrophotometer; and

[0072] Transmit the spectral target data from the spectrophotometer to a color formulation system, particularly via a network.

[0073] There is also provided a computer-implemented method for determining optical data according to a third aspect. In the method, optical data to be used in color formulation is determined, the optical data representing the light transmission characteristics of a plurality of colorants under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, and the method employs a first database and a second database. The method includes:

[0074] Retrieve spectral calibration data from a second database, the spectral calibration data representing the spectral responses of a plurality of calibration samples under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, and at least one measurement condition in the second set being different from at least one measurement condition in the first set;

[0075] Perform a conversion of the spectral calibration data in the second database into converted calibration data, the converted calibration data representing the predicted spectral responses of the calibration samples under the first set of measurement conditions; and

[0076] Calculate optical data based on the conversion-based calibration data and store the calculated optical data in a first database, where the calculated optical data represents the light transmission characteristics of a plurality of colorants under a first set of measurement conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Preferred embodiments of the present invention are described below with reference to the accompanying drawings, which are for the purpose of illustrating the preferred embodiments of the present invention and not for limiting the present invention. In the drawings,

[0078] Figure 1 a highly schematic representation of a color formulation system and an associated method according to a first embodiment is shown;

[0079] Figure 2 a highly schematic representation of a color formulation system and an associated method according to a second embodiment is shown;

[0080] Figure 3 a hardware-oriented schematic block diagram of a color formulation system according to an embodiment of the present invention is shown;

[0081] Figure 4 a schematic illustration of a user interface is shown;

[0082] Figures 5A - 5C schematic diagrams of three different 45 / 0 geometries are shown;

[0083] Figure 6A and 6B schematic diagrams of two different integrating sphere geometries are shown; and

[0084] Figure 7 a schematic diagram of a multi-angle measurement geometry in a multi-angle spectrophotometer is shown. DETAILED DESCRIPTION

[0085] DEFINITIONS

[0086] In the present disclosure, references in the singular form may also include the plural. Specifically, unless the context indicates otherwise, the word "a" or "an" may refer to one, or one or more.

[0087] In this document, the term "lamp" is used to denote any type of light source. It is to be understood that the term "lamp" encompasses not only incandescent lamps, but also other types of light sources such as discharge lamps, LEDs, lasers, etc. A lamp may be configured as a "directed" light source that illuminates a measurement point on a measurement surface from a range of well-defined narrow illumination directions within a small solid angle, or it may be configured as a "diffuse" light source that illuminates the measurement point from a range of large continuous illumination directions defining a relatively large solid angle.

[0088] A "spectrophotometer" is a device for determining the response of a surface or material at multiple different wavelengths or spectral bands, in reflection and / or transmission, under visible and / or UV light illumination.

[0089] The term "fixed angle geometry" is to be understood as referring to a geometry in which a sample (target or calibration sample) is illuminated with a directional light source at a single polar angle relative to the surface normal of the sample and is observed along a single direction. An example of a fixed angle geometry is the well-known "45 / 0" geometry, in which the sample is illuminated at an angle of 45° relative to the surface normal and is observed along the surface normal. A spectrophotometer capable of making measurements in a fixed angle geometry is called a fixed angle spectrophotometer. Specifically, a spectrophotometer configured to perform spectral measurements in a 45 / 0 geometry is called a 45 / 0 spectrophotometer. Examples of 45 / 0 spectrophotometers include the models i1 Paint, 962, and 964 available from X-Rite.

[0090] The term "multi-angle geometry" is to be understood as referring to a geometry in which the sample is illuminated and observed under multiple different fixed angle geometries, and separate spectral data are measured for each of these fixed angle geometries. A spectrophotometer capable of making measurements in a multi-angle geometry is called a multi-angle spectrophotometer. Examples of multi-angle spectrophotometers include the benchtop model TAC7 or the handheld models MA-T6 or MA-T12 available from X-Rite.

[0091] The term "integrating sphere geometry" is to be understood as referring to a geometry in which the sample to be measured is placed below the measurement port of an integrating sphere that has a measurement port, at least one inlet port for illumination, and at least one outlet port for observation. A spectrophotometer having an integrating sphere geometry is called an integrating sphere spectrophotometer. Examples of integrating sphere spectrophotometers are the models Ci7860 and Ci7500 from X-Rite. If the outlet (or equivalently, the inlet port) is arranged at an angle of 8° relative to the surface normal of the sample, the integrating sphere geometry is called "D / 8". If a gloss trap is arranged in the specular direction of the outlet port (or equivalently, the inlet port) to exclude the specular contribution to the total reflectance, the integrating sphere geometry is denoted as "specular excluded" or simply "spex". If no gloss trap is present, the geometry is denoted as "specular included" or "spin".

[0092] The term "visual appearance" or simply "appearance" is to be understood broadly as the way an object reflects and transmits light, including but not limited to how an individual perceives the color and surface texture of an object under various viewing conditions. Appearance also includes instrumental measurements of how an object reflects and transmits light. One aspect of visual appearance is color. The "color" of an object is determined by the portion of the spectrum of incident white light that is reflected or transmitted without being absorbed. Another aspect of visual appearance can be texture. The term "texture" is to be understood broadly as referring to the spatial variation in the appearance of the surface of a material, both at the micro- or meso-scale (i.e., at scales where individual structural elements are generally not distinguishable by the naked eye) and at the macro-scale (i.e., at scales where individual structural elements are distinguishable by the naked eye). Texture, as understood in the present disclosure, includes phenomena such as roughness, shimmer, and variations in surface topography.

[0093] The term "colorant" is to be understood as a component of a material that provides the appearance of color when light is reflected from or transmitted through it. Colorants include pigments and dyes. A "pigment" is a colorant that is generally insoluble in the base component material. Pigments can be of natural or synthetic origin. Pigments can contain organic and inorganic components. The term "pigment" also encompasses so-called "effect pigments" that produce special effects in a material. Examples include interference pigments and reflective particles or flakes. A "dye" is a colorant that is generally soluble in the base component material.

[0094] The term "formulation" is to be understood as a collection of information regarding how to prepare a material. The material can include coating materials (such as automotive paints), solid materials (such as plastic materials), semi-solid materials (such as gels), and combinations thereof. The formulation specifically includes the concentrations of the components (such as bases and colorants) that make up the material. A material that has been prepared according to a formulation can also be referred to as a "preparation".

[0095] The term "database" refers to an organized collection of data that can be electronically accessed by a computer system. In a simple embodiment, a database can be a searchable electronic file in any format. Examples include Microsoft Excel TM spreadsheets or searchable PDF documents. In a more complex embodiment, a database can be a relational database maintained by a relational database management system using a language such as SQL. A database can be maintained locally in a single storage device, or it can be maintained in the form of data distributed across multiple possible remote storage devices.

[0096] The term "computer" or "computing device" refers to any device that can be instructed via a program to automatically perform a sequence of arithmetic or logical operations. A computer can take the form of a desktop computer, laptop computer, tablet computer, smartphone, programmable digital signal processor, etc., but is not limited thereto. A computer typically includes at least one processor and at least one memory device. A computer can be a subunit of another device, such as a spectrophotometer. A computer can be configured to establish a wired or wireless connection to another computer (including a computer for querying a database). A computer can be configured to be coupled via a wired or wireless connection to a data input device, such as a keyboard or computer mouse, or to a data output device, such as a display or printer.

[0097] The term "computer system" is to be understood broadly to encompass one or more computers. If a computer system includes more than one computer, these computers do not necessarily need to be in the same location. The computers in a computer system can communicate with each other via a wired or wireless connection.

[0098] A "processor" is an electronic circuit that performs operations on an external data source, particularly a memory device.

[0099] A "storage device" or simply "memory" is a device for storing information for use by a processor. A memory device can include volatile memory, such as random access memory (RAM), and non-volatile memory, such as read-only memory (ROM). In some embodiments, the memory device can include non-volatile semiconductor memory devices, such as (E)EPROM or flash devices, which can take the form of, for example, a memory card or solid state drive. In some embodiments, the memory device can include a mass storage device having mechanical components, such as a hard disk. A memory device can store programs for execution by a processor. A non-volatile memory device can also be referred to as a non-volatile computer-readable medium.

[0100] A "program" is a set of instructions that can be executed by a processor to perform a specific task.

[0101] A "wired connection" is a connection via an electrical conductor. A wired connection can include one or more cables. A "wireless connection" is a connection that includes the electromagnetic transmission of information between two or more points that are not connected by an electrical conductor. Wireless connections include connections via WiFi TM , Bluetooth TM , 3G / 4G / 5G mobile networks, optical communication, infrared, etc.

[0102] First Embodiment: Measurement conditions for the target sample are different from those required by the formulation engine

[0103] Figure 1FIG. 0 shows a highly schematic representation of a color formulation system 100 and associated method according to a first embodiment. Spectral target data 111 is obtained by performing spectral measurements on a target sample 110 using a spectrophotometer having a first set of measurement conditions. The first set of measurement conditions includes a first measurement geometry (“geometry #1”) and a first set of illumination characteristics (“illuminant #1”).

[0104] The formulation system includes a formulation engine 102 configured to predict one or more formulations 140 of candidate materials expected to match the appearance of the target sample 110. The formulation engine 102 can make this prediction by minimizing a suitable difference norm between the appearance of the target sample 110 and the predicted appearance of the candidate materials. For example, the difference norm can be ΔE in the well-known CIELAB color space or any other difference norm that may be proprietary to the vendor. To be able to select the formulation expected to provide the best match, the formulation engine 102 can also calculate the prediction error for each of the predicted formulations. The prediction error can be expressed as any suitable color difference norm, which may or may not be the same difference norm as that used for optimization.

[0105] To perform the prediction task, the formulation engine 102 accesses optical data 104 representing the light transmission characteristics of a plurality of colorants under a second set of measurement conditions. The second set of measurement conditions includes a second measurement geometry (“geometry #2”) and a second set of illumination characteristics (“illuminant #2”). The second set of measurement conditions is different from the first set of measurement conditions. In particular, the second measurement geometry can be different from the first measurement geometry. For example, the second measurement geometry can be a multi-angle geometry, while the first measurement geometry can be an integrating sphere geometry. Additionally or alternatively, the second set of illumination characteristics can be different from the first set of illumination characteristics. In particular, the spectral characteristics can be different, i.e., illuminant #2 can have a different spectrum from illuminant #1.

[0106] The formulation engine 102 expects to have as its input target data acquired under the second set of measurement conditions. However, the target data 111 has been acquired under the first set of measurement conditions. Therefore, the use of the formulation engine 102 would generally be impossible or would result in incorrect results.

[0107] However, to make it possible to use the formulation engine 102 with the target data 111, the formulation system 100 includes a conversion engine 101. The conversion engine 101 receives the spectral target data 111 and converts them into converted target data 112. The converted target data 112 now represents the expected or predicted spectral response of the target sample 110 under the second set of measurement conditions.

[0108] The converted spectral target data 112 can now be provided to the formulation engine 102 for performing its prediction tasks. A large number of formulation algorithms are known in the prior art, and different algorithms are customized for different types of materials and different measurement conditions.

[0109] Examples of conversions that can be performed by the conversion engine 102 will be further described below.

[0110] Determination of optical data

[0111] The optical data 104 represents the light transmission characteristics of a plurality of colorants. In particular, the optical data 104 can include the absorption coefficient and / or scattering coefficient of each of the multiple spectral components of each colorant in the colorant.

[0112] In some embodiments, the optical data 104 can further include information about hot stamping, i.e., about the way in which the colorant concentration in the material affects the surface reflection properties of the material. For example, some colorants may cause roughness of the surface at high concentrations, which makes the surface reflection more diffuse than at lower concentrations. In some embodiments, the optical data 104 can include information about the directional characteristics of the colorants. For example, effect pigments such as metal flakes can cause strong specular reflection on the surface of the effect pigment, resulting in a glitter effect. In some embodiments, the optical data 104 can include information about how strongly a colorant can affect the directional characteristics of other colorants. In some embodiments, the optical data 104 can include information about non-linear effects, i.e., effects that depend non-linearly on the colorant concentration. In some embodiments, the optical data 104 can include information about the interaction of the colorant with the substrate, e.g., how strongly the colorant can penetrate into the substrate.

[0113] The optical data 104 may have been obtained with the help of calibration data 121, which has been determined by performing spectral measurements on a large number of calibration samples 120 using a spectrophotometer under a second set of measurement conditions.

[0114] To determine the calibration data 121, the calibration samples 120 are typically prepared as follows: for each colorant, a plurality of calibration samples are prepared, each calibration sample including the colorant in a neutral binder at different concentrations. In addition, calibration samples containing the colorant in the binder as well as white and / or black pigments can be prepared. For ink or paint coatings, calibration samples on different substrates (e.g., black and white substrates or rough and smooth substrates) can be additionally prepared. In the case of paint coatings, calibration samples with different paint coating thicknesses can be prepared. In some cases, calibration samples containing a mixture of a certain colorant with other colorants can be prepared.

[0115] For each calibration sample in the calibration samples, one or more spectral measurements are performed to obtain associated calibration data.

[0116] The calibration data 121 of all calibration samples 120 is fed into a calibration engine 103, which calculates optical data 104 based on the calibration data 121. Algorithms for determining optical data based on calibration data are well known in the art. The most suitable algorithm mainly depends on the type of material of the calibration sample and the type of colorant used.

[0117] Ideally, the optical data determined in this way would be pure material constants, completely independent of the measurement conditions under which the calibration data has been obtained. However, in reality, the optical data shows a substantial dependence on the measurement conditions. Therefore, these optical data can usually only be used together with a formulation engine that receives target data obtained under the same set of measurement conditions at its input. Otherwise, the predicted formulation will not result in a good match with the appearance of the target sample.

[0118] Typically, the optical data is determined in a laboratory environment and provided to the formulation system 100 in a database, i.e., the formulation system 100 will usually not include the calibration engine 103. For this reason, the calibration engine 103 is Figure 1 indicated by a dashed line. Then, the formulation engine 102 accesses the optical data 104 in the database.

[0119] Second Embodiment: Measurement conditions for the calibration sample are different from those required by the formulation engine

[0120] Figure 2 A highly schematic representation of a color formulation system 100 and an associated method according to a second embodiment is shown. As in the first embodiment, spectral target data 111 is obtained by performing spectral measurements on a target sample 110 using a spectrophotometer under a first set of measurement conditions.

[0121] In this example, the formulation engine 102 expects the target data obtained under the first set of measurement conditions as its input, i.e., the formulation engine 102 is designed to work directly with the spectral target data 111 without prior conversion. However, to perform its prediction task, the formulation engine requires optical data that represents the light transmission characteristics of various colorants under the first set of measurement conditions. Typically, this would require calibration data obtained under the first set of measurement conditions.

[0122] However, in Figure 2In the example, only the calibration data 121 that has been obtained under the second set of measurement conditions is available. However, in order to be able to use the formulation engine 102, the conversion engine 101 is provided again. In this example, the conversion engine 101 converts the spectral calibration data 121 into converted calibration data 122, which represents the expected spectral response of the calibration sample 120 under the first set of measurement conditions.

[0123] The calibration engine 103 now calculates the optical data 104 based on the converted calibration data 122. The resulting optical data 104 now represents the light transmission characteristics of various colorants under the first set of measurement conditions and can be used by the formulation engine 102 to perform its prediction task.

[0124] In addition, if the calibration data that has been obtained under the first set of measurement conditions is also available, the corresponding optical data can be calculated directly from such calibration data and can also be used by the formulation engine 102. Such optical data can be stored in a third database.

[0125] For example, the formulation engine 102 can be designed to receive data from a new generation of spectrophotometers, while the calibration data for a large number of colorants may have been obtained using a previous generation ("legacy generation") of spectrophotometers. The conversion engine 101 can be used to convert this calibration data in order to obtain optical data that is compatible with the new generation of spectrophotometers. Calibration data for additional colorants can now be obtained using the new generation of spectrophotometers and can be used to generate additional optical data in the third database.

[0126] Computer System: Exemplary Hardware

[0127] Figure 3 FIG. illustrates an exemplary hardware-oriented block diagram of a color formulation system. In this example, the color formulation system includes two main components: a server computer 300 and a remote client computer 400, which can be located remotely from the server computer 300.

[0128] The various components of the server computer 300 communicate with each other via one or more buses 301, as is well known in the art. The server computer 300 includes one or more processors 310. As is well known in the art, the processor 310 may include, for example, a single-core or multi-core CPU and GPU. The server computer 300 further includes one or more non-volatile memory devices 320, such as flash devices and / or hard disk drives. The non-volatile memory 320 stores, in particular, the operating system 321 of the server computer 300 and several application programs, including software for implementing the conversion engine 101, the configuration engine 102, and the calibration engine 103. The non-volatile memory 320 further stores user data as well as the first, second, and third databases 323, 324, and 325. The server computer 300 further includes a random access memory (RAM) 330, and an input / output (I / O) interface 340 and a communication interface 350. The communication interface 350 may include, for example, one or more of an Ethernet interface, a WiFi interface, a Bluetooth TM interface, etc. The communication interface can be used to communicate with the remote client 400.

[0129] The remote client 400 may be configured similarly to the server computer 300. It includes software that executes a user interface for controlling the server computer 300. Communication between the server computer 300 and the client computer 400 can be carried out via a wired or wireless network (e.g., via a LAN or WAN, especially via the Internet). The server computer 300 and / or the client computer 400 may further communicate with the spectrophotometer 200.

[0130] In other embodiments, the server computer is replaced by a cloud computer, and the physical components of the cloud computer are distributed at different physical locations. For example, the databases 323-325 may be implemented in a distributed manner away from the other components. In still other embodiments, the computer system consists only of a single computer that executes all of the software components mentioned above.

[0131] Example of user interface

[0132] Figure 4 A simple user interface 410 that can be implemented on the client computer 400 is shown in a highly schematic manner. The user interface prompts the user to first select a measurement device (spectrophotometer) for determining spectral target data. In this example, the user interface presents a list of known devices to the user, and the user selects one of them. If the configuration system can automatically identify the source of the target data (e.g., because the output of the measurement device contains an indication of the brand and model of the device), this part of the user interface can be omitted or simply replaced by an indication of the device identified by the configuration system.

[0133] The user interface then prompts the user to indicate the type of material that has been measured with the measuring device. In this example, the user interface presents a list of possible materials to the user, and the user selects one of them. In this example, the user has selected "ink".

[0134] Then, the user interface prompts the user to indicate the substrate to which the material is applied. In this example, the user interface presents a list of substrates for the known ink to the user, and the user selects one of them. In this example, the user has selected "substrate B".

[0135] Then, the user interface prompts the user to indicate the set of colorants to be used for formulation. In this example, the user has selected "colorant set B".

[0136] The user interface now prompts the user to indicate the formulation engine that will be used to predict the formulation. In this example, the user interface presents a list of formulation engines that can be used with the spectral target data and the specified type of target material to the user. Here, the user interface takes into account that some formulation engines may not be suitable for directly using with the spectral target data and the available calibration data, but can only be used after the conversion of the spectral target data (first embodiment) or the calibration data (second embodiment). If the user selects a formulation engine that requires conversion, the user interface can remind the user of this situation and confirmation may be required.

[0137] Of course, the above implementation of the user interface is only highly schematic and overly simple, and more complex implementations of the user interface are possible.

[0138] Applications in cloud environment

[0139] The different formulation engines presented to the user can be physically implemented in different computers. The user does not need to know where the formulation engine is physically implemented. From the user's perspective, the formulation engine can simply be provided as a service in the cloud.

[0140] Traditionally, the formulation software has been provided as a locally installed application running on a computer at the user's site. However, recently, providers of the formulation software have started to provide the formulation software as a cloud-based service.

[0141] Making the formulation software available in the cloud provides exciting new possibilities. For example, a library of formulation engines with associated optical data and / or associated calibration data can be stored in the cloud, and new formulation engines with associated data can be added to the library at any time without the need to update the software on the user's computer system. The user can have the opportunity to select the most suitable formulation engine and associated optical data or calibration data from the library for a specific formulation task. For example, the user can choose between a new version and a previous version of a certain formulation engine.

[0142] The above conversion provides the necessary tools for using different formulation engines, which are designed to use data acquired under different sets of measurement conditions, even if the target data is only available for other measurement conditions.

[0143] Example of conversion

[0144] The basis for all conversions is knowledge of at least selected aspects of light transmission in the material of the target sample. In particular, certain properties of the bidirectional reflectance distribution function (BRDF) of the material of the target sample can be used for conversion. In Figure 1 and 2 this knowledge is incorporated in the material information 131. The material information 131 can include entries for one or more of the following information:

[0145] · Information about the type of colorant associated with the spectral input data.

[0146] · Information about the binder of one or more materials associated with the spectral input data.

[0147] · Information about the surface properties of the material associated with the spectral input data.

[0148] · Information about the refractive properties of the material associated with the spectral input data.

[0149] · Information about the substrate associated with the spectral input data.

[0150] · Information about a suitable BRDF model that can be used with the spectral input data.

[0151] · If the spectral input data relates to a layered sample: then information about the layer structure of the material and the properties of at least one layer, such as smoothness, refractive index, composition, reflectance, transmittance.

[0152] · Isotropic or anisotropic. For anisotropic materials, the BRDF values vary according to the rotation of the sample around its surface normal, while for isotropic materials they do not.

[0153] · Fluorescent properties of the colorant. For non-fluorescent materials, the illuminant for the measurement conditions can be neglected. For fluorescent materials, a model can be employed that switches from illumination conditions with UV to those without UV.

[0154] · Ability to maintain polarization: Here, considerations similar to those for fluorescence apply. Materials that do not maintain polarization allow for direct conversion. Those with polarization effects may require adaptation for the conversion.

[0155] Some of the material information 131 can also be used by the formulation engine 102 to correctly perform its prediction task. In particular, the material information can further include information about one or more pre-existing formulations that produce an approximate match to the appearance of the target sample 110. The formulation engine can be configured to correct these pre-existing formulations rather than calculating new formulations from scratch. Correction algorithms are well-known in the field of color formulation.

[0156] The BRDF is a four-dimensional function ρ(θ i , φ i ; θ 0 , φ 0 ), which describes the ratio of the reflected radiance at the inclination angle θ 0 and azimuth angle φ 0 to the incident radiance from the inclination angle θ i and azimuth angle φ i . Expressed in terms of solid angle, we can also write the BRDF as a function ρ(ω i , ω 0 ), where ω i indicates the solid angle at which the radiation is incident on the sample surface, and ω 0 indicates the solid angle into which the radiation is reflected.

[0157] The BRDF is defined only for directions over the hemisphere Ω above the material. For all of the following descriptions of the conversion, we will assume that the appearances of the target sample 110 and the calibration sample 120 can be represented by the BRDF. That is, their materials or the substrates to which they are applied are opaque, or the measurements for determining the target data 111 and the calibration data 121 are performed by placing the material on an opaque backing. No light passes through the combination of the material, substrate, and backing. We will always consider ρ as the combined BRDF of the material, substrate, and backing.

[0158] a) Between different 45 / 0 geometries

[0159] The 45 / 0 geometry is an example of a fixed-angle geometry. A spectrophotometer device with measurement conditions summarized by the term "45 / 0" can have quite different lamp arrangements. Figure 3 Three such arrangements are illustrated in A-3C.

[0160] In Figure 3 A, a single lamp 202 is arranged to illuminate a measurement point on the surface of sample 203 at an angle of 45° relative to the surface normal. Light that has been reflected from the measurement point at an angle of 0° (i.e., parallel to the surface normal) is detected by detector 201. This geometry is commonly denoted as 45as45 (i.e., illuminating at 45° relative to the surface normal and detecting from the specular direction at a specular reflection angle of 45° towards the direction of the incident light), or as 45 / 0.

[0161] In Figure 3 B, three lamps 202 are distributed on a circle so as to illuminate the measurement point at an angle of 45° relative to the surface normal from three different azimuthal directions.

[0162] In Figure 3 C, an annular lamp 204 is used so as to uniformly illuminate the measurement point at an angle of 45° relative to the surface normal from the entire 360° range of azimuthal directions.

[0163] The common feature of all of these is the 45° angle between the direction of the incident light on the sample and the surface normal of the sample. In the case of an isotropic BRDF, the measured values of all different types of 45 / 0 devices can be used interchangeably without any conversion.

[0164] b) Between different diffuse reflection geometries

[0165] Integrating sphere spectrophotometers illuminate a material sample from all sides. They use a white diffuse reflecting sphere that scatters the light emitted by the lamp onto the sample through many different paths. In many of these devices, there is a so-called gloss trap in the specular direction from the detector. This can be closed - i.e., behaving like the rest of the sphere, or open - in which case no light will reach the detector from the specular reflection direction, i.e., as a reflection on the smooth surface of the material sample.

[0166] This is illustrated in Figure 4 A and 4B. In each of these figures, an integrating sphere spectrophotometer is schematically illustrated. The integrating sphere spectrophotometer includes a hollow sphere 206 having a diffuse reflecting white inner surface. The integrating sphere results in a uniform scattering or diffusing effect. Through multiple scattering reflections, the light incident on any point on the inner surface is evenly distributed to all other points. The influence of the original direction of the light is minimized. The sphere 206 has an inlet port at which the lamp is arranged, and an outlet port at which the detector 201 is arranged. The outlet port is arranged at an angle of 8° with respect to the surface normal of the sample 203. This geometry is commonly referred to as "D / 8". In Figure 4In an embodiment of A, the gloss trap 205 is arranged in the direction of the mirror surface at the exit port to exclude the mirror contribution to the total reflectance ("mirror exclusion" or simply "spex"). In Figure 4 In an embodiment of B, there is no gloss trap, and the light that has been reflected from the mirror direction at the sample surface can reach the detector 201 ("mirror inclusion" or "spin").

[0167] To convert the measurement data between two different arrangements ("spex" and "spin"), the surface properties of the material sample need to be known. For a smooth surface, the percentage of light reflected into the ideal mirror direction is described by the Fresnel equations. They require the angle of incidence as well as the refractive indices of the material and the surrounding medium. Then, the percentage of reflected light R surf (for unpolarized incident light) is:

[0168]

[0169] where R surf,P and R surf,S are the reflectivities of p-polarized light and s-polarized light respectively. n 1 is the refractive index of the surrounding medium, and n 2 is the refractive index of the material. In our case, the surrounding medium is usually air, so n 1 is 1.0, and n 2 must be known. θ i is the angle between the direction of the incident light and the surface normal. In the case of a D / 8 measuring device, θ i = 8°. θ t is the corresponding angle after refraction at the material surface. This can be calculated using Snell's law.

[0170] If the material has a smooth surface, the reflected light R surf is the only difference between the measured values of mirror inclusion and mirror exclusion:

[0171] R spin = R surf + R spex

[0172] For this reason, R spex can be calculated from R spin by a simple conversion, and vice versa, that is, by adding R surf to R spex or subtracting R spin from R surf respectively.

[0173] The material information that needs to be known is the refractive index n 2 of the material at the surface of the target object.

[0174] c) Between multi - angle and 45 / 0 or diffuse geometries

[0175] The multi-angle device provides much more information about the material sample by acquiring multiple reflection spectra R for different geometric configurations g ∈ G. Here, G is the set of all measurement geometries captured by the device. g , where G is the set of all measurement geometries captured by the device.

[0176] Figure 5 schematically illustrates a multi-angle spectrophotometer. The lamp 202 is arranged to illuminate a measurement point on the surface of the sample 203 in a plurality of different directions relative to the surface normal. In many known devices, these directions are all in the same plane, i.e., the azimuth angles of all the lamps are the same. However, an arrangement with lamps in different azimuth angle directions has also been proposed, which is particularly useful for measuring samples composed of optically anisotropic materials. One or more detectors 201 measure the reflected light from the surface along one or more detection directions.

[0177] The material information 131 about the material and the substrate can be used to select a suitable parametric bidirectional reflectance distribution function (BRDF) ρ with parameter c c . Assuming that the number of BRDF model parameters is less than or equal to the number of measurement geometries, the parameters can be fitted to the measured reflection spectra by minimizing the sum of distances.

[0178]

[0179] A common choice for the distance function would be L 2 or L 1 norm.

[0180] In the case of an anisotropic BRDF, G should include off-plane geometries, i.e., not all of the detectors 201 and lamps 202 of the multi-angle spectrophotometer should lie in the same plane in space. Alternatively, multiple measurements of the sample 203 captured in different azimuth angle directions can be used during the fitting.

[0181] Once the parameter c has been determined, the parametric BRDF model ρ c can be used to predict the reflectance ω i 、ω 0 for any new pair of solid angles.

[0182] This provides the possibility to convert multi - angular spectral data into spectral data that would be expected to be obtained by different multi - angular spectrophotometers, by a 45 / 0 spectrophotometer, or by an integrating - sphere spectrophotometer. In the case of different multi - angular spectrophotometers or 45 / 0 spectrophotometers, the BRDF can simply be evaluated for the desired geometry. It should be noted that different multi - angular spectrophotometers can have more measurement geometries than the multi - angular spectrophotometer initially used to acquire the multi - angular spectral data. In the case of the diffuse integrating - sphere geometry, the converted spectral data can be calculated as the integral over the entire hemisphere Ω (“spin”) of the incident - light directions above the sample surface or excluding the specular direction Θ = Ω\ω r (“spex”). The excluded solid angle ω r is known from the instrument geometry.

[0183]

[0184] The material information 131 that needs to be known includes the information required to select an appropriate BRDF model.

[0185] d) Between 45 / 0 and diffuse geometries

[0186] In some materials, the material appearance is formed by specular reflection at the smooth surface and diffuse flux within the material itself. If the scattering within the material is sufficiently isotropic, the diffuse flux can be considered a constant R int . Then, the spectral reflectance measured by a 45as45 instrument (i.e., one detector at 0° and one lamp incident at 45°) is:

[0187] R 45as45 = (1 - ·F 45 )(1 - F 0 )R int cos(θ t(45) )

[0188] According to the Fresnel equation as described above, the term 1 - F θ represents the percentage of light incident at angle θ that passes through the interface and enters the material. Due to reciprocity, the percentage of light that exits the material at angle θ and enters the detector can be expressed in the same way.

[0189] Note that since the detector is not located in the specular - reflection direction, the percentage of light reflected at the interface is omitted from the equation. θ t(45) is the corresponding angle after refraction at the material surface. This can be calculated using Snell's law.

[0190] As long as ω i is the same as ω 0The reflected (specular) directions have a sufficiently large angular separation, and the equation can be easily generalized to other measurement geometries ω i , ω 0 :

[0191]

[0192] Similarly, the spectral reflectance measured through a diffusing sphere with a gloss trap ("spex") is:

[0193]

[0194] As before, since the specular reflection direction of the detector is covered by the gloss trap, the percentage of light reflected at the interface is omitted, which particularly prevents any light from reaching the surface from this direction.

[0195] Both of these equations can now be solved for R int , and used to calculate R 45as45 from R spex , and vice versa:

[0196]

[0197] Via the considerations discussed above in part b), further conversions from R spin and to R spin are possible.

[0198] As stated above in part a), further conversions from other 45 / 0 geometries and to other 45 / 0 geometries are possible.

[0199] The required material information is the refractive index n for calculating the Fresnel equations 2 and the subsurface scattering properties for confirming the existence of the internal diffusive flux R int .

[0200] Note that in the same spirit, not only can R 45as45 be calculated from R spex or R spin . In fact, if the above assumptions regarding the light scattering properties (specular reflection plus diffusive flux) remain reasonably good approximations, then the expected spectral reflectance for any arbitrary measurement geometry ω i , ω 0 can be calculated from R spex or R spin , or equally well, from R 45as45 . In this way, it is possible to convert spectral data obtained using diffusive geometries or 45 / 0 geometries into spectral data obtained using a multi-angle spectrophotometer.

[0201] Of course, other assumptions regarding the light scattering properties can be made. For example, the Phong model, Blinn model, or Cook-Torrance model can be adopted, where certain additional assumptions are made regarding the parameters of the corresponding model such that only one free parameter is retained for each wavelength interval, and then this free parameter can be determined by measurements with a single geometry.

[0202] This discussion emphasizes that it is possible to convert sparse spectral data obtained at only one or a few measurement geometries into more complex spectral data related to a large number of measurement geometries.

[0203] e) Between multi - angle and 45 / 0 or diffuse geometries (continued)

[0204] If the multi-angle device has a geometry with a detector at 0° and a lamp at 45°, then its spectral reflectance measurement can be directly used as R 45as45 and, in the case where the material has an isotropic BRDF, can be used interchangeably for any 45 / 0 measurement, as explained above in part a).

[0205] If the multi-angle device does not have such a geometry, or the material exhibits anisotropic behavior, then the parametric BRDF model can be fitted to the multi-view data, as described above in part c), and used to calculate the spectral reflectance measurement R 45as45 = ρ c (45°, 0°; 0°, 0°) or for the circumferential configuration.

[0206] If the target data 102 from the multi-angle device and the calibration data 107 from the 45 / 0 instrument are available, then the measured or calculated R of the multi-angle device 45aS45 can be used as the target measurement 103 for conversion. For the opposite case, i.e., the target data 102 obtained under the 45 / 0 geometry and the multi-angle calibration data 107, the R measured or calculated from the multi-angle calibration data 45aS45 can be used as the calibration data 109 for conversion. As described above, it is also conceivable to apply the conversion in the opposite direction, e.g., calculating the multi-angle target or calibration data from the 45 / 0 target or calibration data respectively. In the same spirit, the conversion between multi-angle spectral data and diffuse data can be performed in either direction.

[0207] f) Between different illumination characteristics

[0208] In the same spirit, conversions can also be applied to account for different illumination conditions. In particular:

[0209] · Fluorescence: When there are fluorescent components in the material, the color of the material can no longer be represented by a simple wavelength-dependent percentage between the incident and the outgoing fluxes. However, studies have shown that fluorescence is non-directional, i.e., it is an isotropic effect. That is to say, if the fluorescent components are fully characterized, the conversion of measurement data between two measurement conditions with different lamp spectral power distributions can be considered independently of the geometric arrangement of the detector and the lamp.

[0210] · If the spectral target data 111 or the calibration data 121 are acquired under directional illumination from a fixed direction (e.g., in a 45 / 0 geometry or a multi-angle geometry), and if the material information 131 indicates that the material surface maintains the polarization of light, the polarization part of the Fresnel equations can be used in the conversion engine 101.

[0211] Modification

[0212] Many modifications are possible without departing from the scope of the invention as defined in the appended claims.

[0213] In particular, the method can be easily extended to consider not only color but also other aspects of appearance, such as texture or angular appearance properties.

Claims

1. A color formulation system (100) comprising a conversion engine (101), a formulation engine (102) and a first database (323), wherein the color formulation system (100) is configured to receive spectral target data (111) that represents the spectral response of a target sample (110) under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, wherein the first database (323) includes optical data (104) that represents the light transmission characteristics of a plurality of colorants under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, with at least one measurement condition in the second set being different from at least one measurement condition in the first set, wherein the conversion engine (101) is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being the spectral target data (111), and the converted spectral data being converted target data (112) that represents the predicted spectral response of the target sample (110) under the second set of measurement conditions, and wherein the formulation engine (102) is configured to use the converted target data (112) and the optical data (104) in the first database (323) to predict a formulation (140) of a candidate material that is expected to match the appearance of the target sample (110).

2. The color formulation system (100) according to claim 1, further comprising: a second database (324) that includes spectral calibration data (121) that represents the spectral responses of a plurality of calibration samples (120) under the second set of measurement conditions, and a calibration engine (103) that is configured to calculate the optical data (104) based on the calibration data (122) in the second database (324) and store the calculated optical data (104) in the first database (323).

3. A color formulation system (100) comprising a conversion engine (101), a formulation engine (102), a calibration engine (103), a first database (323) and a second database (324), wherein the color formulation system (100) is configured to receive spectral target data (111) that represents the spectral response of a target sample (110) under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, wherein the second database (324) includes spectral calibration data (121) that represents the spectral responses of a plurality of calibration samples (120) under the second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, with at least one measurement condition in the second set being different from at least one measurement condition in the first set, wherein the conversion engine (101) is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being spectral calibration data (121) in a second database (324), and the converted spectral data being converted calibration data (122) representing the predicted spectral response of a calibration sample (120) under a first set of measurement conditions, wherein the calibration engine (103) is configured to calculate optical data (104) based on the converted calibration data (122) and store the calculated optical data (104) in a first database (323), the calculated optical data (104) representing the light transmission characteristics of a plurality of colorants under a first set of measurement conditions, and wherein the formulation engine (102) is configured to use spectral target data (111) and optical data (104) in the first database (323) to predict a formulation (140) of a candidate material, the candidate material being expected to match the appearance of a target sample (110).

4. The color formulation system according to claim 3, further comprising a third database (325), the third database (325) including additional optical data that has been calculated from spectral calibration data (121), the spectral calibration data (121) representing the spectral response of the same or different calibration samples (120) under a first set of measurement conditions, wherein the formulation engine (102) is configured to use the third database (325) in addition to the first database (323) to predict the formulation (140).

5. The color formulation system according to any one of the preceding claims, comprising a plurality of formulation engines (102), wherein, the color formulation system includes a user interface (410), the user interface (410) being configured to prompt a user to select a formulation engine to be used for predicting a formulation.

6. The color formulation system according to any one of the preceding claims, wherein, the conversion engine (101) is configured to receive material information (131) and take the material information (131) into account when performing the conversion, wherein the material information (131) includes at least one of the following information items: - information about the type of colorant associated with the spectral input data; - information about the binder of one or more materials associated with the spectral input data; - information about the surface properties of the material associated with the spectral input data; - information about the refractive properties of the material associated with the spectral input data; - information about the scattering properties of the material associated with the spectral input data; - information about the fluorescence properties of the material associated with the spectral input data; - information about the polarization properties of the material associated with the spectral input data; - information about the substrate on which the material associated with the spectral input data has been applied; and - information about one or more pre-existing formulations that produce an approximate match to the appearance of the target sample (110).

7. The color formulation system according to any one of the preceding claims, wherein the first and second measurement geometries are integrating sphere geometries, one of the integrating sphere geometries being mirror-including type and the other being mirror-excluding type, and wherein the conversion includes calculating a refraction term and adding the refraction term to the spectral input data or subtracting the refraction term from the spectral input data, the refraction term representing the percentage of light reflected in the direction of the mirror.

8. The color formulation system according to any one of claims 1-6, wherein the conversion engine is configured to perform the following steps: Based on the spectral input data, determine at least one parameter of a model of light transport, in particular a BRDF model; Calculate the converted spectral data using the model of light transport.

9. The color formulation system according to claim 8, wherein the measurement geometry associated with the spectral input data is a first multi-angle geometry, and the measurement geometry associated with the converted spectral data is a second multi-angle geometry, a fixed angle geometry, in particular a 45 / 0 geometry or an integrating sphere geometry.

10. The color formulation system according to any one of claims 1-6, wherein one of the first and second measurement geometries is an integrating sphere geometry and the other is a fixed angle geometry, in particular a 45 / 0 geometry, and wherein the conversion includes calculating an integral over the percentage of light that can enter and leave the material associated with the spectral input data, the integral being performed over a hemisphere, and associating the integral with the percentage of light that can enter and leave the material under the fixed angle geometry.

11. A system for determining optical data to be used in color formulation, the optical data representing the light transport characteristics of a plurality of colorants under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, the system comprising: a conversion engine (101), a calibration engine (103), a first database (323) and a second database (324), wherein the second database (324) includes spectral calibration data (121) representing the spectral responses of a plurality of calibration samples (120) under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, at least one measurement condition in the second set being different from at least one measurement condition in the first set, wherein the conversion engine (101) is configured to receive spectral input data and perform a conversion of the spectral input data into converted spectral data, the spectral input data being the spectral calibration data (121) in the second database (324), and the converted spectral data being converted calibration data (122) representing the predicted spectral responses of the calibration samples (120) under the first set of measurement conditions, wherein the calibration engine (103) is configured to calculate optical data (104) based on the transformed calibration data (122) and store the calculated optical data (104) in a first database (323), the calculated optical data (104) representing the light transmission characteristics of a plurality of colorants under a first set of measurement conditions.

12. A computer-implemented method for color formulation, the method comprising: receiving spectral target data (111) that represents the spectral response of a target sample (110) under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, retrieving from a first database (323) optical data (104) that represents the light transmission characteristics of a plurality of colorants under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, at least one measurement condition in the second set being different from at least one measurement condition in the first set; performing a transformation of the spectral target data (111) into transformed target data (112), the transformed target data (112) representing the expected spectral response of the target sample (110) under the second set of measurement conditions, and using the transformed target data (112) and the optical data (104) in the first database (323) to predict a formulation (140) of a candidate material that is expected to match the appearance of the target sample (110).

13. A computer-implemented method for color formulation, the method using a first database (323) and a second database (324), the method comprising: receiving spectral target data (111) that represents the spectral response of a target sample (110) under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, retrieving from a second database (324) spectral calibration data (121) that represents the spectral response of a plurality of calibration samples (120) under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, at least one measurement condition in the second set being different from at least one measurement condition in the first set; performing a transformation of the spectral calibration data (121) into transformed calibration data (122), the transformed calibration data (122) representing the expected spectral response of the calibration samples (110) under the first set of measurement conditions, calculating optical data (104) based on the transformed calibration data (122) and storing the calculated optical data (104) in the first database (323), the calculated optical data (104) representing the light transmission characteristics of a plurality of colorants under the first set of measurement conditions, and using the spectral target data (112) and the optical data (104) in the first database (323) to predict a formulation (140) of a candidate material that is expected to match the appearance of the target sample (110).

14. A computer-implemented method for determining optical data to be used in a color formulation, the optical data representing the light transmission characteristics of a plurality of colorants under a first set of measurement conditions, the first set of measurement conditions including at least one first measurement geometry and at least one first illumination characteristic, the method employing a first database (323) and a second database (324), the method comprising: retrieving spectral calibration data (121) from the second database (324), the spectral calibration data (121) representing the spectral responses of a plurality of calibration samples (120) under a second set of measurement conditions, the second set of measurement conditions including at least one second measurement geometry and at least one second illumination characteristic, at least one measurement condition in the second set being different from at least one measurement condition in the first set; performing a conversion of the spectral calibration data (121) in the second database (324) into converted calibration data (122), the converted calibration data (122) representing the predicted spectral responses of the calibration samples (120) under the first set of measurement conditions; and calculating optical data (104) based on the converted calibration data (122) and storing the calculated optical data (104) in the first database (323), the calculated optical data (104) representing the light transmission characteristics of the plurality of colorants under the first set of measurement conditions.

Images