Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
  • G01J3/52—Measurement of colour; Colour measuring devices, e.g. colorimeters using colour charts
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
  • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
  • G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
  • G01J3/52—Measurement of colour; Colour measuring devices, e.g. colorimeters using colour charts
  • G01J3/524—Calibration of colorimeters
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/90—Determination of colour characteristics
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
  • H04N1/46—Colour picture communication systems
  • H04N1/56—Processing of colour picture signals
  • H04N1/60—Colour correction or control
  • H04N1/603—Colour correction or control controlled by characteristics of the picture signal generator or the picture reproducer
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
  • H04N1/46—Colour picture communication systems
  • H04N1/56—Processing of colour picture signals
  • H04N1/60—Colour correction or control
  • H04N1/603—Colour correction or control controlled by characteristics of the picture signal generator or the picture reproducer
  • H04N1/6033—Colour correction or control controlled by characteristics of the picture signal generator or the picture reproducer using test pattern analysis
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N17/00—Diagnosis, testing or measuring for television systems or their details
  • H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras

Definitions

• the present invention is generally related to the verification and calibration of color as well as corrective adjustments for an imaging device.
• Color is basically what the human visual system perceives on receiving various wavelengths of light that have reflected off objects. This color recognition is also known as the spectral sensitivity of the human visual system. To express the perceived colors numerically, many methods have been developed of which one of them is the XYZ tristimulus values as developed by an international organization known as "Commission Internationale de I'Eclairge” (CIE). The XYZ tristimulus values are based on the theory that the human visual system possesses receptors for three primary colors red, green, and blue and that all the colors perceived are mixtures of these three primary colors.
• CIE Commission Internationale de I'Eclairge
• Figure 1 illustrates the spectral sensitivity corresponding to the human visual system in terms of XYZ tristimulus values.
• imager device response channels were to exactly duplicate the XYZ tristimulus values, in theory, that imager device could be capable of exactly duplicating the colors seen by the visual system.
• imager device due to the complexities involved in producing such an imager device, it is not practical to exactly duplicate the XYZ tristimulus values.
• Figure 2 illustrates an exemplary red, green and blue response of an imager device. It is desirable to transform the response to be as closely correlating as possible to the XYZ tristimulus values so that the imager device may channel outputs that closely correspond to the color seen by the human visual system.
• This is the function of color calibration that is performed on the imager device.
• the function of the color calibration is to find a color calibrating matrix (e.g., a 3x3 matrix) that brings the response of the image sensor as close as possible (i.e., least squares error) to that of the XYZ tristimulus values.
• a color calibrating matrix e.g., a 3x3 matrix
• An exemplary method of determining the color calibrating 3x3 matrix is to take several reflective color targets of known XYZ tristimulus values such as the Macbeth Colorchecker® targets available from Macbeth /Kollmorgen Instruments Corporation in New Windsor, New York, that represent twenty-four colors and generally depict the colors in various regions of the color space.
• XYZ tristimulus values such as the Macbeth Colorchecker® targets available from Macbeth /Kollmorgen Instruments Corporation in New Windsor, New York, that represent twenty-four colors and generally depict the colors in various regions of the color space.
• RGB red, green and blue
• the imager device uses the imager device to be calibrated, twenty-four color targets are read by the imager which generates the corresponding RGB values. Note that each X Y Z tristimulus values for the color targets are known.
• the measured RGB values are loaded into a measured data matrix (MEAS), an example being:
• the 3x3 color calibrating matrix can be further specified as:
• Mn ,. . ., M33 are the color calibrating coefficients of the color calibrating matrix.
• MEAS 7 refers to the transpose of MEAS matrix.
• ( ) ⁇ denotes an inverse.
• Xn, Yn, Zn are XYZ tristimulus values of the respective targets n.
• the color calibrating coefficients are selected to provide the minimized least squares error that corresponds to the best fit for mapping the RGB values of the imager device into the XYZ tristimulus values of the color targets. It may not be immediately apparent why the coefficients obtained through this method would provide the least squares error and further discussion can be found in Box, Hunter and Hunter, "Statistics for Experimenters” (John Wiley and Sons, New York, 1978) at page 498-502. It is desirable that the coefficient values be calculated and stored with a minimum of three significant digits of accuracy. Note that as long as the proper luminance is provided, the magnitudes of the coefficients are not important but only the ratios between the coefficients. Thus, the matrices: 16.645 7.013 1.253 33.29 14.026 2.506 ⁇
• the targets may fade with constant exposure to light requiring special storage during non-use and frequent change out.
• a reference light source corresponding to CIE D65 illumination is provided that needs to be constantly checked for color temperature and intensity.
• a fairly large production area must be allocated so that an appropriate target distant relationship exists with the image sensor under calibration. Therefore, what is needed is a method and apparatus for producing the color calibration coefficients or color calibrating matrix without the difficulties associated with the use of reflective color targets.
• a method and apparatus for color calibrating an imager device is disclosed.
• the imager device is subjected to a plurality of light sources.
• Color channel responses are obtained from the imager device and the color calibrating coefficients are determined.
• Figure 1 illustrates the spectral sensitivity corresponding to the human eye in terms of XYZ tristimulus values
• Figure 2 illustrates an example of a red, blue and green (RGB) response of an imager device
• Figure 3 depicts an exemplary embodiment of the present invention illustrating the calibration instrument
• Figure 4 illustrates an exemplary embodiment of the present invention
• Figure 5 illustrates an exemplary embodiment of the present invention that uses statistical correlation
• Figure 6 illustrates an exemplary table used for correlating the imager devices' responses
• Figure 7 illustrates an exemplary embodiment of the present invention that uses sets of weighting factors
• Figure 8 illustrates another exemplary embodiment of the present invention using sets of weighting factors.
• the present invention uses a set of light emitting diodes (LEDs) or other light sources to provide a stimuli for the imager device to produce a color calibration matrix.
• LEDs light emitting diodes
• the light sources will be understood to mean originating light sources.
• the present invention can be practiced with both CMOS and CCD devices as well as other imager devices.
• RGB red, green and blue
• CCMY cyan magenta yellow
• CYG cyan, magenta, yellow, green
• the responses from the various color systems will be collectively referred to as color channel responses.
• 3x3 matrix other matrices may be used, for example, 3xn matrix or mxn matrix, where m, n are integers, depending on the nature of the color calibration.
• FIG. 3 illustrates an exemplary embodiment of the invention.
• the calibration instrument 10 comprises a chamber 12 with an aperture 16 to allow an imager device 18 to be calibrated to have access to the interior of the chamber 12.
• a plurality of LEDs or other light sources 14 that provide the stimuli for the imager device 18.
• the LEDs or other light sources 14 may be controlled by a computer 20 according to the exemplary methods to be described.
• the computer 20 may further receive RGB outputs from the imager device 18 and using those output values, run a color calibration program to produce a 3x3 color calibrating matrix for the imager device 18.
• the produced 3x3 color calibrating matrix may stored in a memory device such as read only memory (ROM) within the imager device 18 and is subsequently used to map the read RGB values into corresponding XYZ tristimulus values.
• ROM read only memory
• the calibration instrument 10 to calibrate the various imager devices, reflective color targets are no longer needed.
• the LEDs or other light sources 14 the same calibration result can be obtained as would have been with the color targets.
• five LEDs are used although more than five LEDs or as low as three LEDs may be used depending on the color region to be covered.
• peak emission wavelengths at 430, 470, 545, 590 and 660 nm are used to provide the stimuli that gives the result as would be obtained by means of calibration with reflective color targets corresponding to the Macbeth Colorchecker® targets.
• peak wavelengths may be used depending on the particular desired results to be achieved.
• the particular peak wavelengths have been chosen to meet the wavelengths of LEDs that are commercially available while also providing strong correlation with the color coefficients of the color calibrating matrix.
• Figure 4 illustrates an exemplary embodiment of the present invention.
• the color calibration to produce the 3x3 color calibrating matrix is directly applied to the RGB values produced by the five LEDs.
• the embodiment does not calibrate by correlating with the Macbeth Colorchecker® targets and may be sufficient to provide adequate color accuracy, depending upon the calibration requirements.
• the method is as follows:
• Block 41 illustrates the steps of determining the XYZ tristimulus values of the LEDs. This may be done through the use of a spectrophotometer. Let the five LEDs be represented by:
• XDI, YDI, ZQI is the XYZ Tristimulus Value for LED#1 XD2, YD2, Z D2 is the XYZ Tristimulus Value for LED#2 XD3, YD3, Z D3 is the XYZ Tristimulus Value for LED#3 XD4, YD4, ZD4 is the XYZ Tristimulus Value for LED#4 XD5, YDS, ZD5 is the XYZ Tristimulus Value for LED#5 Block 42 illustrates the imager device to be calibrated being illuminated by the five LEDs sequentially and the RGB responses recorded.
• RDI, GDI, BQI is the imager RGB response to LED#1 RD2, GD2, BD2 is the imager RGB response to LED#2 RD3, GD3, B03 is the imager RGB response to LED#3 RD4, GD4, BD4 is the imager RGB response to LED#4 RDS, GD5, Bos is the imager RGB response to LED#5
• Block 43 illustrates the recorded RGB responses being loaded into a MEAS matrix:
• Block 44 illustrates the color calibrating coefficients (i.e. M ⁇ ,...,M33) of the 3x3 matrix are determined by:
• Figure 5 illustrates another exemplary embodiment of the present invention. This example involves statistical correlation.
• Figure 6 illustrates a table 60 showing a plurality of tested imager devices' responses 61 to the twenty-four reflective color targets of the Macbeth Colorchecker®, which are defined by the color calibrating coefficients of the 3x3 matrix 63 being plotted with the corresponding imager devices' RGB responses to the five LEDs 65. Once sufficient imager devices are calibrated, the accumulated data is then used to determine the statistical correlation between the results from the five LEDs and coefficients of the 3x3 matrix.
• Figure 5 will now illustrate the procedure in more detail.
• Block 51 illustrates an imager device to be calibrated being exposed to twenty-four reflective color targets corresponding to the Macbeth Colorchecker®. However, other color targets may be used provided the XYZ tristimulus values of the targets are known. Further, the number of targets may be varied according to a desired result.
• Block 52 illustrates a 3x3 color calibrating matrix being constructed from the read RGB values of the twenty-four targets. This procedure has been previously described in the Background Information section above. Let the 3x3 matrix resulting from the color calibration using the color targets be represented by:
• Mn ,. . ., M33 are the color calibrating coefficients of the color calibrating matrix.
• Block 53 illustrates the same imager device being stimulated by a series of five LEDs and the RGB responses for the five LEDs recorded.
• the imager device under test is illuminated sequentially by all five LEDs and a total of fifteen responses are recorded. Taking the five LEDs employed to be LED#1 - LED#5, let RDI represent the imager's red channel response to LED#1. Similarly, let GQI represent the imager's green channel response to LED#1 and so forth.
• the imager device responses can be represented as:
• RDI, GDI, BQI is the imager RGB response to LED#1 RD2, GD2, BD2 is the imager RGB response to LED#2 D3, GD3, BD3 is the imager RGB response to LED#3 RD4, GD4, BD4 is the imager RGB response to LED#4 RDS, GD5, Bos is the imager RGB response to LED#5
• Block 54 illustrates blocks 51 to 53 being repeated until a desired number of imager devices have been calibrated. From the combined accumulated data, a table such as the one illustrated in Figure 6 may be constructed. Blocks 55 to 56 illustrate the steps that once enough data has been accumulated, polynomial regression may be used to determine the correlation between the results of the color targets and the LEDs. Polynomial regression is based on the theory that through the use of simultaneous equations, a correlation between measured variable responses can be approximated. Polynomial regression is well known in linear algebra, however, further discussion of the polynomial regression method in the context of imager devices may be found in Henry R. Kang, "Color Technology for Electronic Imaging Devices" (SPIE Optical Engineering Press) at pages 55-62.
• n P ⁇ + Pl*£ D1+P2*G Dl+P3*£ D1+P4*R D2+P5*G D2+P6*B D2+P7*R D3
• the values Po , ... , Pi 5 represent statistically determined correlation coefficients.
• the correlation coefficients may be determined through well known statistical methods or alternatively, by using a statistics analysis program such as JMP commercially available from SAS Institute,
• Block 59 illustrates that once the set of equations correlating the coefficients of the 3x3 matrix and the imager device response to five LEDs is obtained, the color targets are no longer necessary and the set of equations is used for subsequent color calibration of the imager devices.
• the advantage of this approach is that the color calibration coefficients are determined through indirect comparison with a "golden standard" without the continuing use of the Macbeth Colorchecker®.
• Figure 7 illustrates another embodiment of the present invention.
• This example involves the simulation of colors of the Macbeth Colorchecker® through a combination of LEDs lighted simultaneously.
• a combination of the LED lights powered simultaneously according to sets of weighting factors give the same color characteristics as the Macbeth Colorchecker® targets under standard CIE D65 illumination.
• the simulated colors are presented one at a time to the imager device and the associated RGB responses are recorded.
• the color calibration is then performed in a similar manner as that performed when using color targets. This color calibration procedure has been described in the Background Information section above.
• Block 71 illustrates the step of determining the XYZ tristimulus values of the LEDs. This may be done through the use of a spectrophotometer. Let the five LEDs be represented as:
• XDI, YDI, Z D I is the XYZ Tristimulus Value for LED#1 D2 , YD2 , Z D2 is the XYZ Tristimulus Value for LED#2 X D3 Y D3, Z D3 is the XYZ Tristimulus Value for LED#3 XD4 , YD 4, Z D4 is the XYZ Tristimulus Value for LED#4 XD5 , YD S, ZQ5 is the XYZ Tristimulus Value for LED#5
• Block 72 illustrates the step of determining the XYZ tristimulus values of the Macbeth colors to be simulated that are represented as:
• XMaci, YMaci, Zjviaci s tne ⁇ Tristimulus Value for Macbeth Color#l XMac2, YMac2, Z ac2 is the XYZ Tristimulus Value for Macbeth Color#2
• Xfviac24, YMac24, is the ZMac24 - XYZ Tristimulus Value for Macbeth Color#24
• Block 73 illustrates the step of determining a set of weighting factors that is applied to the LEDs to allow simulation of the Macbeth color.
• the relationship can be expressed as:
• the first subscript on the weighting factor / refers to the Macbeth color being matched (i.e., 1-24).
• the second subscript refers to the LED (i.e., 1-5) associated with the weighting factor.
• [Mteo] is the transpose matrix of [MLEO] .
• the five LEDs selected should have the basis which can describe all of the colors of the Macbeth Colorchecker®. However, if this condition is not met exactly, substitute synthesizable colors may be used which are close approximations to the Macbeth colors. Alternately, different light sources could be selected to better span the required color space. In this instance, the five LEDs have peak wavelengths at 430, 470, 545, 590 and 660nm respectively.
• Block 74 illustrates storing the obtained set of weight factors from above.
• Block 75 illustrates blocks 71 to 74 being repeated to find the set of weighting factors for each of the Macbeth colors to be simulated.
• Blocks 76 to 78 illustrate that once the twenty-four sets of weighting factors have been stored corresponding to the respective Macbeth colors, the five LEDs are simultaneously illuminated with the drive power in the proportions indicated by the weighting factors.
• An image of the color is captured by the tested imager device and the RGB responses recorded. In total, twenty-four images are captured to accumulate the total system response to the twenty- four colors of the Macbeth Colorchecker®.
• Block 79 illustrates the responses are then used by the color calibration procedure which has been described in the background information to produce the 3x3 color calibrating matrix.
• Figure 8 illustrates another embodiment of the present invention.
• a knowledge of the system responses to the LED stimulus is employed to determine and predict what the system response would be to the standard Macbeth colors. These predictions are then used to provide the response data required in the color calibration to produce the 3x3 color calibrating matrix.
• the method is premised on linearity of system response, and on the presumption that the LEDs have the basis that can describe all of the colors of the Macbeth Colorchecker®. If this presumption is not met exactly, substitute colors may be used.
• Block 81 illustrates the step of determining the intercept of the tested imager device's RGB responses for zero input. This step is performed to determine the offsets of the imager device to be calibrated. The purpose is to allow more accurate linear interpolation of results. As an example, for an imager device with positive offsets, the correction for offset may be an essentially equivalent to a dark frame subtraction (i.e., offset corresponding to the imager device's response to the dark).
• Block 82 illustrates the step of illuminating the imager device with each of the five LEDs and recording the imager responses for each LED. Let the imager device responses to the LEDs be represented by:
• BBD3 is the imager RGB response to LED#3 D4
• GD4 is the imager RGB response to LED#4 RDS
• GDS BD5 is the imager RGB response to LED#5
• Block 83 illustrates storing the above imager device responses to the five LEDs.
• Block 84 illustrates the step of computing the set of weighting factors associated with each of the twenty-four Macbeth colors. The procedure for determining the set of weighting factors has been described with respect to Figure 7.
• Block 85 illustrates applying the computed sets of weighting factors to the imager device's RGB responses to the five LEDs to determine the equivalent Macbeth color response:
• R24 24l(RDl-R ⁇ )+RO
• Block 86 illustrates the use the computed equivalent responses from above which is loaded into a MEAS table as described in the Background Information section above and the color calibration procedure performed to determine the 3x3 color calibration matrix.
• This method only requires capturing five frames (i.e., one frame for each LED) and yet, is capable of determining the color calibration matrix as if the calibration had been performed using twenty-four reflective color targets.

Landscapes

• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Spectroscopy & Molecular Physics (AREA)
• General Physics & Mathematics (AREA)
• Signal Processing (AREA)
• Multimedia (AREA)
• General Health & Medical Sciences (AREA)
• Biomedical Technology (AREA)
• Health & Medical Sciences (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Theoretical Computer Science (AREA)
• Color Image Communication Systems (AREA)
• Facsimile Image Signal Circuits (AREA)
• Spectrometry And Color Measurement (AREA)

Priority Applications (4)

Applications Claiming Priority (2)

Publications (1)

Family

ID=22292126

Family Applications (1)

Country Status (9)

Cited By (5)

Families Citing this family (53)

Citations (1)

Family Cites Families (18)

• 1998
  • 1998-06-23 US US09/102,875 patent/US6205244B1/en not_active Expired - Lifetime
• 1999
  • 1999-06-03 TW TW088109217A patent/TW436611B/zh not_active IP Right Cessation
  • 1999-06-10 AU AU44354/99A patent/AU4435499A/en not_active Abandoned
  • 1999-06-10 JP JP2000556331A patent/JP4216474B2/ja not_active Expired - Fee Related
  • 1999-06-10 KR KR10-2000-7014527A patent/KR100437583B1/ko not_active Expired - Fee Related
  • 1999-06-10 WO PCT/US1999/013163 patent/WO1999067738A1/en not_active Ceased
  • 1999-06-10 GB GB0031144A patent/GB2353913B/en not_active Expired - Fee Related
  • 1999-06-10 DE DE19983327T patent/DE19983327C2/de not_active Expired - Fee Related
  • 1999-06-10 CN CNB998078484A patent/CN1210672C/zh not_active Expired - Fee Related
• 2000
  • 2000-08-18 US US09/642,388 patent/US7016533B1/en not_active Expired - Fee Related
• 2005
  • 2005-11-18 US US11/283,375 patent/US7382918B2/en not_active Expired - Fee Related

Patent Citations (1)

Also Published As

Similar Documents

Legal Events