Image processing algorithm for characterization of uniformity of printed images

• US 6,571,000 B1
• Filed: 11/29/1999
• Issued: 05/27/2003
• Est. Priority Date: 11/29/1999
• Status: Expired due to Fees

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Dalal, Edul N., Mishra, Bimal, Rasmussen, D. Rene
• Primary Examiner(s): Chang, Jon
• Assistant Examiner(s): Kim, Charles
• Application Number:
  US09/450,180
• Time in Patent Office: 1,275 Days
• Field of Search: 382/112, 382/162-167, 382/191, 382/195, 382/202, 382/224, 382/274, 382/275, 382/280, 358/1.9, 358/3.26, 358/518, 358/530
• US Class Current: 382/112
• CPC Class Codes: G06T 2207/10008 from scanner, fax or copier
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/30144 Printing quality
• CPC Class Codes: G06T 7/001 using an image reference approach
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern
• 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 1/00015 Reproducing apparatus
• CPC Class Codes: H04N 1/00023 Colour systems
• 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 1/00061 using a remote apparatus H04N1/00066 takes precedence
• CPC Class Codes: H04N 1/00063 using at least a part of the apparatus itself, e.g. self-testing
• CPC Class Codes: H04N 1/00084 Recovery or repair, e.g. self-repair

Abstract

This invention specifically covers one of the many image quality (IQ) metrics that can be part of an overall image quality analysis engine. The specific problem with image quality addressed with this metric is that of a region of a printed (or copied) image, which was intended to have a uniform color, but which shows visible color variations. The color variation can have many different forms, both with respect to the type of color difference and with respect to the spatial nature of the non-uniformities. The image quality analysis system readily distinguishes between non-uniformities in the following different categories, including: (a) amplitude modulated cluster dot halftone patterns; (b) frequency modulated halftone patterns (e.g., stochastic screens); (c) irregular two-dimensional variations from noise; (d) isolated (non-periodic) one-dimensional streaks; (e) periodic, one-dimensional bands; and (f) two-dimensional periodic variations (Moire). The results from the image quality analysis can then be used as a basis for diagnosing machine problems.

Claims

1. An image quality analysis System for determination color non-uniformities in an image output device, comprising:

a test pattern pertinent to image quality determination;

a scanner that scans a hardcopy test image, which has been generated by the output device based on the test pattern, to form a digital raster image; and

an image quality analysis module that receives the digital raster image, distinguishes one or more test targets from the digital raster image and detects color non-uniformities, uniformities, and performs image quality analysis on the test budgets to obtain results quantifying image quality,

wherein the image quality analysis module includes means for categorizing detected color non-uniformities as either structured or non-structured variances, the means for categorizing including;

means for converting the image to a complex valued image represented as an amplitude and phase; and

means for generating a binary image mask from the complex valued image by dividing a frequency range of the complex valued image into at least a low range and a high range,

wherein pixels of the image in the low range arc categorized as non-structured variances and pixels of the image in the high range are compared against average amplitude and standard deviation and categorized as structured variances if the comparison deviates by more than a predetermined threshold.

2. The image quality analysis system of claim 1, wherein the structured variances are further categorized into periodic components and one-dimensional components.

3. The image quality analysis system of claim 1, wherein the means for categorizing includes image converting means for converting a color image to a monochrome image representing luminance.

4. The image quality analysis system of claim 3, wherein the means for categorizing further includes means for calculating average amplitude and standard deviation for pixels of the monochrome image.

5. The image quality analysis system of claim 1, wherein the image quality analysis module further includes means for applying a human visual model to the digital raster image.

6. The image quality analysis system of claim 1, wherein the image quality analysis module resides locally at a site of the image output device.

7. The image quality analysis system of claim 6, wherein the image output device is a digital copier that contains the scanner.

8. The image quality analysis system of claim 6, further comprising a communication module that connects the image quality analysis module to a remote facility.

9. The image quality analysis system of claim 1, wherein the scanner and the image quality analysis module reside remote from the image output device.

10. The image quality analysis system of claim 1, further comprising a diagnostic module that can diagnose and correct color non-uniformities detected and categorized by the image quality analysis module.

22. The method of claim 1, further comprising providing the categorized structured and non-structured variations of color non-uniformity to a diagnostic module to correct the color non-uniformity.

11. A method of performing image quality analysis on an image output device having an output station that generates a hardcopy image from a digital image, the method comprising:

generating a hardcopy image output from the image output device based on a predetermined test pattern;

scanning the hardcopy image using a scanner to form a digital raster image;

performing image quality analysis on test targets of the image to determine color non-uniformities;

; and

categorizing determined color non-uniformities into structured and non-structured variances, wherein the categorizing includes;

converting the image to a complex valued image represented as an amplitude and phase;

generating a binary image mask from the complex valued image by dividing a frequency range of the complex valued image into at least a low range and a high range;

categorizing pixels in the low range as non-structured variances; and

comparing pixels in the high range with average amplitude and standard deviation and categorizing pixels as structured variances if the comparison deviates by more than a predetermined threshold.

12. The method of claim 11, comprising further categorizing the color non-uniformities into periodic components and one-dimensional components.

13. The method of claim 11, further comprising applying a human visual filter to the image.

14. The method of claim 13, wherein the step of categorizing into structured and non-structured variances occurs prior to applying the human visual filter.

17. The method of claim 11, wherein the step of performing image quality analysis includes converting the digital raster image from a color image to a monochrome image representing luminance.

15. A method of performing image quality analysis on an image output device having an output station that generates a hardcopy image from a digital image, the method comprising:

generating a hardcopy image output from the image output device based on a predetermined test pattern;

scanning the hardcopy image output from the image output device based on a predetermined test pattern;

scanning the hardcopy image using a scanner to form a digital raster image having one or more test targets;

performing image quality analysis on the test targets to determine color non-uniformities;

categorizing determined color non-uniformities into structured and non-structured variances;

after categorizing the determined color non-uniformities, applying a human visual filter to the image; and

separating the human visual filtered image into structured and non-structured components.

16. The method of claim 15, further comprising categorizing the structured and non-structured components into low and high frequency variations.

18. A method of performing image quality analysis on an image output device having an output station that generates a hardcopy image from a digital image, the method comprising:

generating a hardcopy image output from the image output device based on a predetermined test pattern;

scanning the hardcopy image using a scanner to form a digital raster image;

performing image quality analysis on test targets of the image to determine color non-uniformities by converting the digital raster image from a color image to a monochrome image representing luminance and calculating average amplitude and standard deviation for pixels of the monochrome image over a predefined frequency range;

categorizing determined color non-uniformities into structured and non-structured variances; and

categorizing individual pixels as structured or non-structured based on a comparison of the individual pixel with the calculated standard deviation.

19. The method of claim 18, further comprising applying an inverse transform to non-structured pixels and subtracting the resultant NSPY image from the monochrome image to form a SPY image.

20. The method of claim 19, further comprising determining banding or halftone screening from the SPY image.

21. The method of claim 19, further comprising blurring the NSPY image and determining mottle and graininess.