Automated image quality control

• US 5,600,574 A
• Filed: 05/13/1994
• Issued: 02/04/1997
• Est. Priority Date: 05/13/1994
• Status: Expired due to Term

• Current Assignee: Eastman Kodak Company
• Original Assignee: Minnesota Mining and Manufacturing Co (3M Company)
• Inventors: Reitan, Ronald C.
• Primary Examiner(s): Trammell, James P.
• Assistant Examiner(s): Wachsman, Hal D.
• Application Number:
  US08/242,275
• Time in Patent Office: 998 Days
• Field of Search: 364/552, 364/550, 364/413.13, 364/413.14, 364/413.19, 364/525, 382/100, 382/128, 382/131, 382/132, 382/141, 382/149, 382/190, 382/224, 358/406, 358/405, 358/462, 358/504
• US Class Current: 702/185
• CPC Class Codes: A61B 6/583 using calibration phantoms
• CPC Class Codes: H04N 1/00002 Diagnosis, testing or measuring; Detecting, analysing or monitoring not otherwise provided for error detection, error correction or monitoring in digital computers or digital computer components G06F11/00
• CPC Class Codes: H04N 1/00013 Reading apparatus
• CPC Class Codes: H04N 1/00015 Reproducing apparatus
• CPC Class Codes: H04N 1/00029 Diagnosis, i.e. identifying a problem by comparison with a normal state
• CPC Class Codes: H04N 1/00045 using a reference pattern designed for the purpose, e.g. a test chart
• CPC Class Codes: H04N 1/00053 out of service, i.e. outside of normal operation
• CPC Class Codes: H04N 1/00063 using at least a part of the apparatus itself, e.g. self-testing
• CPC Class Codes: H04N 1/00076 locally
• CPC Class Codes: H04N 1/4078 using gradational references, e.g. grey-scale test pattern analysis
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A system, apparatus and method for testing the functional components of an electronic digital imaging system is described. The system includes apparatus for image acquisition, storage, display, communication and printing. The system relies on a closed loop analysis to test system components by measuring a set of statistical image quality metrics. The expected set of statistics are in the form of special purpose features stored as a data set representative of an expected reference object. The closed loop analysis measures, for example, the quality of the printing component of the system by outputting a copy of the expected reference image, using the acquisition component to input the copy of the expected reference image, and then comparing the statistics against threshold values representative of an ideally operating component. The comparison of statistics against the threshold values provides a go/no-go measure of component performance and can indicate sources of system degradation.

Claims

1. A method of automatically measuring the performance of a plurality of functional components of an electronic digital imaging system, comprising the steps of:

storing metric thresholds for known good functional components including an image input device and image output device and an image storage memory;

acquiring a sample image from a plurality of reference objects to produce a set of pixel data representative of the plurality of reference objects;

measuring selected attributes from the set of pixel data for image acquisition, image storage and image output functional components and producing therefrom a set of feature statistics;

comparing the set of feature statistics against the metric thresholds; and

indicating it any one of the set of feature statistics fall below any one of the metric thresholds and indicating the failure of the functional component which resulted in the any one of the set of feature statistics to fall below any one of the metric thresholds.

2. The method according to claim 1 further including the step of locating a specific failure of a specific functional component which will cause any one of the set of feature statistics to fall below the metric thresholds.

3. The method according to claim 2 further including the step of indicating the cause of failure of the specific functional component which caused a particular one of the set of feature statistics to fall below a corresponding particular one of the metric thresholds.

4. The method according to claim 1 further including the steps of

storing a data set of reference features corresponding to the set of feature statistics;

and

producing the reference object from the data set of reference features for use in measuring the output modality of the electronic digital imaging system.

5. The method according to claim 1 further including the step of calibrating the functional components of the electronic digital imaging system by adjusting values in look up tables each corresponding to each of the system components.

6. The method according to claim 1 further including the step of acquiring the sample image from a reference film as the reference object.

7. The method according to claim 1 further including the step of acquiring the sample image from a three dimensional phantom as the reference object.

8. A method of measuring the geometric distortion in test images processed by the plurality of functional components of an electronic digital imaging system, comprising the steps of:

providing a two-dimensional reference object;

acquiring a sample image from the reference object to produce a set of pixel data representative of the reference object;

locating a registration indicator in the set of pixel data representative of the reference object;

locating a specific region of interest in the set of pixel data representative of the reference object based on the location of the registration indicator;

processing a subset of the set of pixel data within the specific region of interest;

measuring a selected feature within the subset of the set of pixel data within the specific region of interest to produce a measured value;

comparing the measured value against an expected performance value; and

indicating if the measured value falls below the expected performance value to indicate a failure in one of the plurality of functional components.

9. The method according to claim 8 further including the step of producing the reference object from a stored data set of reference features to measure the output functional component of the electronic digital imaging system.

10. The method according to claim 8 further comprises measuring the linearity of a line in the reference object, including the substeps of:

performing a region-of-interest calculation on the pixel data within the subset of pixel data to produce a one-pixel width line;

calculating a best fit line to the one-pixel width line; and

measuring the maximum deviation of the one-pixel width line from the best fit line as a measurement of the linear geometric performance of the functional components of the electronic digital imaging system.

11. The method according to claim 8 further including the step of acquiring the sample image from a reference film as the reference object.

12. The method according to claim 8 further including the step of acquiring the sample image from a three dimensional phantom as the reference object.

13. A method of measuring the pixel value integrity in images processed by the functional components of an electronic digital imaging system, comprising the steps of:

providing a reference object;

acquiring a sample image from the reference object to produce a set of pixel data representative of the reference object;

locating a specific region of interest in the set of pixel data representative of the reference object;

processing a subset of the set of pixel data within the specific region of interest;

measuring a selected feature within the subset of the set of pixel data within the specific region of interest to produce a measured value;

comparing the measured value against an expected performance value; and

indicating if the measured value fall below the expected performance value; and

identifying streaks in the reference object, comprising the substep of performing a two-pass, two dimensional background estimate on the pixel data.

14. An automated image quality control system, comprising:

a plurality of input modalities;

a plurality of output modalities;

an image storage memory;

a processor connected to the image storage memory, the plurality of input modalities and the plurality of output modalities and having means for executing the following computer-executed program steps;

storing metric thresholds for known good system components included in all modalities;

acquiring a sample image from a reference object to produce a set of pixel data representative of a physical reference object;

measuring selected attributes from the set of pixel data and producing therefrom a set of feature statistics;

comparing the set of feature statistics against the metric thresholds;

indicating if any one of the set of feature statistics fall below any one of the metric thresholds and

indicating the failure of a specific system component which resulted in the any one of the set of feature statistics to fall below any one of the metric thresholds.

15. The automated image quality control system of claim 14 further comprising:

a first look up table connected to the plurality of input modalities;

a second look up table connected to the plurality of output modalities;

the processor connected to the first look up table and the second look up table and further having means for executing the following steps;

storing first adjustment values in the first look up table in response to calibration of the plurality of input modalities; and

storing second adjustment values in the second look up table in response to calibration of the plurality of output modalities.

16. The automated image quality control system of claim 15 wherein the processor is further operable for calculating the first adjustment values and the second adjustment values.

17. An automated image quality control system, comprising:

storage means for storing metric thresholds for a plurality of known good system components;

acquisition means for acquiring a sample image from a reference object having a plurality of reference features to produce a set of pixel data representative of the reference features of the reference object;

measurement means for measuring selected attributes from the set of pixel data and producing therefrom a set of feature statistics;

comparison means for comparing the set of feature statistics against the metric thresholds; and

indicator means for automatical indicating a failure if any one of the set of feature statistics fall below any one of the metric thresholds and for indicating a one of the plurality of system components which caused the failure.

18. A method of measuring the geometric distortion in images processed by the functional components of an electronic digital imaging system, comprising the steps of:

producing a reference object from a stored data set of reference features to measure the output functional component of the electronic digital imaging system.;

acquiring a sample image from the reference object to produce a set of pixel data representative of the reference object;

locating a registration indicator in the set of pixel data representative of the reference object;

locating a specific region of interest in the set of pixel data representative of the reference object based on the location of the registration indicator;

processing a subset of the set of pixel data within the specific region of interest;

performing a region-of-interest calculation on the pixel data within the subset of pixel data to produce a one-pixel width line;

calculating a best fit line to the one-pixel width line;

measuring the maximum deviation of the one-pixel width line from the best fit line as a measurement of the linear geometric performance of the functional components of the electronic digital imaging system to produce a measured value;

comparing the measured value against an expected performance value; and

indicating if the measured value falls below the expected value.

19. A method of measuring the pixel value integrity in images processed by the functional components of an electronic digital imaging system, comprising the steps of:

providing a reference object;

acquiring a sample image from the reference object to produce a set of pixel data representative of the reference object;

locating a specific region of interest in the set of pixel data representative of the reference object;

processing a subset of the set of pixel data within the specific region of interest;

measuring a selected feature within the subset of the set of pixel data within the specific region of interest by performing a two-pass, two dimensional background estimate on the pixel data to identify streaks in the reference object and producing thereby a measured value;

comparing the measured value against an expected performance value; and

indicating if measured value falls below the expected performance value.