Dynamic gamut mapping selection

• US 6,947,589 B2
• Filed: 09/20/2001
• Issued: 09/20/2005
• Est. Priority Date: 09/20/2001
• Status: Expired due to Fees

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Kohler, Timothy L., Haikin, John S., Newman, Todd D.
• Primary Examiner(s): Patel, Kanjibhai
• Assistant Examiner(s): Bayat, Ali
• Application Number:
  US09/956,040
• Publication Number:
  US 20030053683A1
• Time in Patent Office: 1,461 Days
• Field of Search: 382/162, 382/167, 382/165, 382/166, 382/170, 382/175, 382/176, 382/180, 382/190, 382/181, 382/275, 382271-273, 382/252, 358518-523, 358/3.01, 358/3.02, 358/3.09, 358/3.1, 358/3.14, 358/3.16, 358/3.21, 358/3.22, 358/3.13, 358/143.16, 345589-590, 345690-697, 348254-256, 348671-679
• US Class Current: 382/162
• CPC Class Codes: G06T 11/001 Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: H04N 1/6058 Reduction of colour to a range of reproducible colours, e.g. to ink- reproducible colour gamut

Abstract

The present invention is directed to a mechanism of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising automatically generating prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to at least one characteristic of the image data, and automatically selecting at least one of the plurality of gamut mapping components based on the prediction information.

Claims

1. A method of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising:

pre-storing weights corresponding to gamut mapping components and image characteristics,

detecting at least one characteristic of the image data;

automatically generating prediction information for use in selecting from a plurality of gamut mapping components based on the pre-stored weights and the detected characteristic of the image data; and

automatically selecting at least one of the plurality of gamut mapping components based on the prediction information.

2. A method according to claim 1, wherein the characteristic is detected based on the colors of the image data.

3. A method according to claim 1, wherein the image data includes metadata, the characteristic is detected based at least in part on the metadata.

4. A method according to claim 3, wherein the metadata is automatically generated during generation of the image data.

5. A method according to claim 4, wherein the image data is generated by an image capture device, and wherein the metadata comprises information associated with the image capture.

6. A method according to claim 5, wherein the image capture information includes one or more of aperture, exposure time and flash usage information.

7. A method according to claim 4, wherein the metadata is user-specified, and wherein the metadata comprises annotations to the image.

8. A method according to claim 1, wherein the characteristic is detected based at least in part on an image pixel hue characteristic.

9. A method according to claim 1, wherein the characteristic is detected based at least in part on an image pixel chroma characteristic.

10. A method according to claim 1, wherein the characteristic is detected based at least in part on a redness characteristic.

11. A method according to claim 1, wherein dynamic selection of a GMA is performed using a neural network.

12. A method according to claim 1, wherein the prediction information comprises at least one predictor of output quality for each of the plurality of gamut mapping components.

13. A method according to claim 12, wherein a gamut mapping component is selected based on the at least one predictor associated with each of the plurality of gamut mapping components for the given predictor.

14. A method according to claim 1, further comprising:

transforming the image data from the first color space to the second color space using the selected gamut mapping component.

15. A method according to claim 14, wherein transformation of the image data is performed in a transformation pipeline, the selection of a gamut mapping component is performed outside the transformation pipeline.

16. A method according to claim 14, wherein transformation of the image data is performed in a transformation pipeline, the selection of a gamut mapping component is performed within the transformation pipeline.

17. A method of claim 1, wherein a value of a pre-stored weight is determined based on a gamut mapping preference corresponding to an image characteristic.

18. A method of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising:

automatically generating prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to at least one characteristic of the image data; and

automatically selecting at least one of the plurality of gamut mapping components based on the prediction information,

wherein the prediction information is based at least in part on a redness characteristic, and wherein the redness characteristic is defined as a proportion of pixels having a CIELAB metric hue angle within a given range and a CIELAB metric chromas that satisfies a given chroma threshold.

19. A method of dynamically selecting a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, the method comprising:

automatically generating prediction information for use in selecting from a plurality of gamut mapping components, wherein the prediction information is based on determined gamut mapping preferences corresponding to at least one characteristic of the image data; and

automatically selecting at least one of the plurality of gamut mapping components based on the prediction information,

wherein the prediction information comprises a colorfulness predictor, and wherein a colorfulness characteristic is defined as a proportion of pixels with CIELAB metric chromas greater than or equal to a colorfulness threshold.

20. A system for dynamically selecting a gamut mapping component for use in a color management system to transform colors specified by image data from a first color space to a second color space, the system comprising:

pre-storing, in a storage medium, weights corresponding to gamut mapping components and image characteristics;

at least one image characterizer configured to automatically detect a characteristic of image data;

selection engine configured to generate prediction information for use in selecting from a plurality of gamut mapping components based on the pre-stored weights and the detected characteristic of the image data, wherein the selection engine is configured to automatically select at least one of the plurality of gamut mapping components based on the prediction information.

21. A system according to claim 20, wherein the prediction information comprises at least one predictor of output quality for each of the plurality of gamut mapping components.

22. A system according to claim 21, wherein a gamut mapping component is selected based on the at least one predictor associated with each of the plurality of gamut mapping components.

23. A system according to claim 20, wherein a value of a pre-stored weight is determined based on a gamut mapping preference corresponding to an image characteristic.

24. Computer-executable program code stored on a computer-readable medium, said computer-executable program code, which dynamically selects a gamut mapping component for use in a color management system which transforms colors specified in image data from a first color space to a second color space, comprises:

code to pre-store weights corresponding to gamut mapping components and image characteristics;

code to detect at least one characteristic of the image data;

code to automatically generate prediction information for use in selecting from a plurality of gamut mapping components based on the pre-stored weights and the detected characteristic of the image data; and

code to automatically select at least one of the plurality of gamut mapping components based on the prediction information.